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: "the dense k subgraph problem"
  Abstract: Given a graph G = (V,E) and a parameter k, we consider the problem of finding a subset U in V of size k that maximizes the number of induced edges (DkS). We improve upon the previously best known approximation ratio for DkS, a ratio that has not seen any progress during the last decade. Specifically, we improve the approximation ratio from n^{0.32258} to n^{0.3159}. #R##N#The improved ratio is obtained by studying a variant to the DkS problem in which one considers the problem of finding a subset U in V of size at most k that maximizes the number of induced edges. #R##N#Finally, we study the DkS variant in which one considers the problem of finding a subset U in V of size at least k that maximizes the number of induced edges.

Connected through hyperedge HE-1 (induced by source paper "inapproximability of maximum biclique problems minimum k cut and densest at least k subgraph from the small set expansion hypothesis"):
  with members:
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "analysis of boolean functions"
    - "analytical approach to parallel repetition"
    - "hardness of vertex deletion and project scheduling"
    - "reductions between expansion problems"
    - "tight approximation ratio of a general greedy splitting algorithm for the minimum k way cut problem"
    - "finding large and small dense subgraphs"

Connected through hyperedge HE-2 (induced by source paper "almost polynomial ratio eth hardness of approximating densest k subgraph"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "partitioning a graph into small pieces with applications to path transversal"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "eth hardness for signaling in symmetric zero sum games"
    - "approximating dense max 2 csps"

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)