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: "maximizing non monotone submodular set functions subject to different constraints combined algorithms"
  Abstract: We study the problem of maximizing constrained non-monotone submodular functions and provide approximation algorithms that improve existing algorithms in terms of either the approximation factor or simplicity. Our algorithms combine existing local search and greedy based algorithms. Different constraints that we study are exact cardinality and multiple knapsack constraints. For the multiple-knapsack constraints we achieve a $(0.25-2\epsilon)$-factor algorithm. #R##N#We also show, as our main contribution, how to use the continuous greedy process for non-monotone functions and, as a result, obtain a $0.13$-factor approximation algorithm for maximization over any solvable down-monotone polytope. The continuous greedy process has been previously used for maximizing smooth monotone submodular function over a down-monotone polytope \cite{CCPV08}. This implies a 0.13-approximation for several discrete problems, such as maximizing a non-negative submodular function subject to a matroid constraint and/or multiple knapsack constraints.

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"
    - "greedy maximization of functions with bounded curvature under partition matroid 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"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-2 (induced by source paper "submodular function maximization via the multilinear relaxation and contention resolution schemes"):
  with members:
    - "symmetry and approximability of submodular maximization problems"
    - "geometric packing under non uniform constraints"
    - "mechanism design via correlation gap"
    - "robust and maxmin optimization under matroid and knapsack uncertainty sets"
    - "a note on concentration of submodular functions"
    - "submodular maximization by simulated annealing"
    - "thresholded covering algorithms for robust and max min optimization"
    - "correlation robust 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.DS(Data Structures and Algorithms)