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: "non monotone dr submodular function maximization"
  Abstract: We consider non-monotone DR-submodular function maximization, where DR-submodularity (diminishing return submodularity) is an extension of submodularity for functions over the integer lattice based on the concept of the diminishing return property. Maximizing non-monotone DR-submodular functions has many applications in machine learning that cannot be captured by submodular set functions. In this paper, we present a $\frac{1}{2+\epsilon}$-approximation algorithm with a running time of roughly $O(\frac{n}{\epsilon}\log^2 B)$, where $n$ is the size of the ground set, $B$ is the maximum value of a coordinate, and $\epsilon > 0$ is a parameter. The approximation ratio is almost tight and the dependency of running time on $B$ is exponentially smaller than the naive greedy algorithm. Experiments on synthetic and real-world datasets demonstrate that our algorithm outputs almost the best solution compared to other baseline algorithms, whereas its running time is several orders of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-2 (induced by source paper "provable non convex optimization and algorithm validation via submodularity"):
  with members:
    - "continuous dr submodular maximization structure and algorithms"
    - "greedy maxcut algorithms and their information content"
    - "beyond convexity stochastic quasi convex optimization"
    - "scalable variational inference in log supermodular models"
    - "maximizing monotone submodular functions over the integer lattice"
    - "non negative principal component analysis message passing algorithms and sharp asymptotics"
    - "selecting the rank of truncated svd by maximum approximation capacity"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-3 (induced by source paper "parallel algorithm for non monotone dr submodular maximization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "continuous dr submodular maximization structure and algorithms"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-4 (induced by source paper "optimal dr submodular maximization and applications to provable mean field inference"):
  with members:
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "submodular function maximization on the bounded integer lattice"
    - "scalable variational inference in log supermodular models"

Connected through hyperedge HE-5 (induced by source paper "a parallel double greedy algorithm for submodular maximization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular maximization with matroid and packing constraints in parallel"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "an optimal approximation for submodular maximization under a matroid constraint in the adaptive complexity model"
    - "non monotone submodular maximization in exponentially fewer iterations"

Connected through hyperedge HE-6 (induced by source paper "fast maximization of non submodular monotonic functions on the integer lattice"):
  with members:
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "boosting information spread an algorithmic approach"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "weakly submodular functions"
    - "lazier than lazy greedy"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-7 (induced by source paper "subspace selection via dr submodular maximization on lattices"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular function maximization over distributive and integer lattices"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "submodular function maximization over distributive and integer lattices"):
  with members:
    - "deterministic algorithms for submodular maximization problems"
    - "submodular function maximization on the bounded integer lattice"
    - "symmetry and approximability of submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "submodular maximization by simulated annealing"
    - "submodular approximation sampling based algorithms and lower bounds"

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)