human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
  Abstract: In this paper we study the adaptivity of submodular maximization. Adaptivity quantifies the number of sequential rounds that an algorithm makes when function evaluations can be executed in parallel. Adaptivity is a fundamental concept that is heavily studied across a variety of areas in computer science, largely due to the need for parallelizing computation. For the canonical problem of maximizing a monotone submodular function under a cardinality constraint, it is well known that a simple greedy algorithm achieves a $1-1/e$ approximation and that this approximation is optimal for polynomial-time algorithms. Somewhat surprisingly, despite extensive efforts on submodular optimization for large-scale datasets, until very recently there was no known algorithm that achieves a constant factor approximation for this problem whose adaptivity is sublinear in the size of the ground set $n$. #R##N#Recent work by Balkanski and Singer describes an algorithm that obtains an approximation arbitrarily close to $1/3$ in $\mathcal{O}(\log n)$ adaptive rounds and shows that no algorithm can obtain a constant factor approximation in $\tilde{o}(\log n)$ adaptive rounds. This approach achieves an exponential speedup in adaptivity (and parallel running time) at the expense of approximation quality. #R##N#In this paper we describe a novel approach that yields an algorithm whose approximation is arbitrarily close to the optimal $1-1/e$ guarantee in $\mathcal{O}(\log n)$ adaptive rounds. This algorithm therefore achieves an exponential speedup in parallel running time for submodular maximization at the expense of an arbitrarily small loss in approximation quality. This guarantee is optimal in both approximation and adaptivity, up to lower order terms.

Connected through hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "towards nearly linear time algorithms for submodular maximization with a matroid constraint"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "streaming algorithms for news and scientific literature recommendation submodular maximization with a d knapsack constraint"

Connected through hyperedge HE-2 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "the fast algorithm for submodular maximization"
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"

Connected through hyperedge HE-3 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "independent sets in vertex arrival streams"

Connected through hyperedge HE-4 (induced by source paper "adaptive sequence submodularity"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "submodularity on hypergraphs from sets to sequences"

Connected through hyperedge HE-5 (induced by source paper "bring your own greedy max near optimal 1 2 approximations for submodular knapsack"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular secretary problem with shortlists"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "do less get more streaming submodular maximization with subsampling"

Connected through hyperedge HE-6 (induced by source paper "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"):
  with members:
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-7 (induced by source paper "non monotone submodular maximization with nearly optimal adaptivity and query complexity"):
  with members:
    - "fast parallel algorithms for feature selection"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"
    - "non monotone submodular maximization in exponentially fewer iterations"

Connected through hyperedge HE-8 (induced by source paper "locally private learning without interaction requires separation"):
  with members:
    - "exponential separations in local differential privacy"
    - "the role of interactivity in local differential privacy"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "collecting telemetry data privately"
    - "distributed mean estimation with limited communication"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "the limitations of optimization from samples"
    - "algorithmic stability for adaptive data analysis"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DS(Data Structures and Algorithms)