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: "weakly submodular functions"
  Abstract: Submodular functions are well-studied in combinatorial optimization, game theory and economics. The natural diminishing returns property makes them suitable for many applications. We study an extension of monotone submodular functions, which we call {\em weakly submodular functions}. Our extension includes some (mildly) supermodular functions. We show that several natural functions belong to this class and relate our class to some other recent submodular function extensions. #R##N#We consider the optimization problem of maximizing a weakly submodular function subject to uniform and general matroid constraints. For a uniform matroid constraint, the "standard greedy algorithm" achieves a constant approximation ratio where the constant (experimentally) converges to 5.95 as the cardinality constraint increases. For a general matroid constraint, a simple local search algorithm achieves a constant approximation ratio where the constant (analytically) converges to 10.22 as the rank of the matroid increases.

Connected through hyperedge HE-1 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "max sum diversity via convex programming"
    - "building a good team secretary problems and the supermodular degree"

Connected through hyperedge HE-2 (induced by source paper "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "deep reinforcement learning of marked temporal point processes"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-3 (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"
    - "non monotone dr submodular function maximization"
    - "boosting information spread an algorithmic approach"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "lazier than lazy greedy"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-4 (induced by source paper "guarantees for greedy maximization of non submodular functions with applications"):
  with members:
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "greedy column subset selection new bounds and distributed algorithms"
    - "maximizing a monotone submodular function with a bounded curvature under a knapsack constraint"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-5 (induced by source paper "approximate supermodularity bounds for experimental design"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "budget feasible mechanisms for experimental design"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-6 (induced by source paper "max sum diversification monotone submodular functions and dynamic updates"):
  with members:
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "sum of squares lower bounds for planted clique"
    - "maximum dispersion and geometric maximum weight cliques"

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.DM(Discrete Mathematics)