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: "greedy maximization of functions with bounded curvature under partition matroid constraints"
  Abstract: We investigate the performance of a deterministic GREEDY algorithm for the problem of maximizing functions under a partition matroid constraint. We consider non-monotone submodular functions and monotone subadditive functions. Even though constrained maximization problems of monotone submodular functions have been extensively studied, little is known about greedy maximization of non-monotone submodular functions or monotone subadditive functions. #R##N#We give approximation guarantees for GREEDY on these problems, in terms of the curvature. We find that this simple heuristic yields a strong approximation guarantee on a broad class of functions. #R##N#We discuss the applicability of our results to three real-world problems: Maximizing the determinant function of a positive semidefinite matrix, and related problems such as the maximum entropy sampling problem, the constrained maximum cut problem on directed graphs, and combinatorial auction games. #R##N#We conclude that GREEDY is well-suited to approach these problems. Overall, we present evidence to support the idea that, when dealing with constrained maximization problems with bounded curvature, one needs not search for approximate) monotonicity to get good approximate solutions.

Connected through hyperedge HE-1 (induced by source paper "non monotone submodular maximization with multiple knapsacks in static and dynamic settings"):
  with members:
    - "optimization of chance constrained submodular functions"
    - "pareto optimization for subset selection with dynamic cost constraints"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "max sum diversification monotone submodular functions and dynamic updates"
    - "maximizing non monotone submodular set functions subject to different constraints combined algorithms"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-2 (induced by source paper "optimization of chance constrained submodular functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "evolutionary algorithms for the chance constrained knapsack problem"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "pareto optimization for subset selection with dynamic cost constraints"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "social dynamics of digg"

Connected through hyperedge HE-3 (induced by source paper "multi objective evolutionary algorithms are still good maximizing monotone approximately submodular minus modular functions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "pareto optimization for subset selection with dynamic cost constraints"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "lazier than lazy greedy"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)