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: "streaming algorithms for submodular function maximization"
  Abstract: We consider the problem of maximizing a nonnegative submodular set function $f:2^{\mathcal{N}} \rightarrow \mathbb{R}^+$ subject to a $p$-matchoid constraint in the single-pass streaming setting. Previous work in this context has considered streaming algorithms for modular functions and monotone submodular functions. The main result is for submodular functions that are {\em non-monotone}. We describe deterministic and randomized algorithms that obtain a $\Omega(\frac{1}{p})$-approximation using $O(k \log k)$-space, where $k$ is an upper bound on the cardinality of the desired set. The model assumes value oracle access to $f$ and membership oracles for the matroids defining the $p$-matchoid constraint.

Connected through hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale"):
  with members:
    - "flexible modeling of diversity with strongly log concave distributions"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "strong log concavity does not imply log submodularity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "data summarization at scale a two stage submodular approach"
    - "a tight approximation for submodular maximization with mixed packing and covering constraints"

Connected through hyperedge HE-2 (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"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"
    - "the power of randomization distributed submodular maximization on massive datasets"

Connected through hyperedge HE-4 (induced by source paper "submodular matroid secretary problem with shortlists"):
  with members:
    - "quasi polynomial algorithms for submodular tree orienteering and other directed network design problems"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "submodular secretary problem with shortlists"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "a logarithmic integrality gap bound for directed steiner tree in quasi bipartite graphs"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "the submodular secretary problem goes linear"

Connected through hyperedge HE-5 (induced by source paper "streaming submodular maximization under a k set system constraint"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an optimal streaming algorithm for non monotone submodular maximization"
    - "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"
    - "data summarization at scale a two stage submodular approach"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"

Connected through hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"

Connected through hyperedge HE-7 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online"):
  with members:
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "submodular secretary problems cardinality matching and linear constraints"

Connected through hyperedge HE-8 (induced by source paper "online non monotone dr submodular maximization"):
  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"
    - "potential function proofs for first order methods"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

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)