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: "a new approximation guarantee for monotone submodular function maximization via discrete convexity"
  Abstract: In monotone submodular function maximization, approximation guarantees based on the curvature of the objective function have been extensively studied in the literature. However, the notion of curvature is often pessimistic, and we rarely obtain improved approximation guarantees, even for very simple objective functions. #R##N#In this paper, we provide a novel approximation guarantee by extracting an M$^\natural$-concave function $h:2^E \to \mathbb R_+$, a notion in discrete convex analysis, from the objective function $f:2^E \to \mathbb R_+$. We introduce the notion of $h$-curvature, which measures how much $f$ deviates from $h$, and show that we can obtain a $(1-\gamma/e-\epsilon)$-approximation to the problem of maximizing $f$ under a cardinality constraint in polynomial time for any constant $\epsilon > 0$. Then, we show that we can obtain nontrivial approximation guarantees for various problems by applying the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"):
  with members:
    - "beyond submodular maximization"
    - "fast parallel algorithms for feature selection"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "greed is still good maximizing monotone submodular supermodular functions"
    - "fast greedy algorithms for dictionary selection with generalized sparsity constraints"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-2 (induced by source paper "guess free maximization of submodular and linear sums"):
  with members:
    - "random order contention resolution schemes"
    - "constrained submodular maximization beyond 1 e"
    - "online contention resolution schemes"
    - "maximizing symmetric submodular functions"
    - "a stochastic probing problem with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-3 (induced by source paper "submodular maximization under a matroid constraint asking more from an old friend the greedy algorithm"):
  with members:
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

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)