human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "k core minimization a game theoretic approach"):
  Abstract: $K$-cores are maximal induced subgraphs where all vertices have degree at least $k$. These dense patterns have applications in community detection, network visualization and protein function prediction. However, $k$-cores can be quite unstable to network modifications, which motivates the question: How resilient is the k-core structure of a network, such as the Web or Facebook, to edge deletions? We investigate this question from an algorithmic perspective. More specifically, we study the problem of computing a small set of edges for which the removal minimizes the $k$-core structure of a network. #R##N#This paper provides a comprehensive characterization of the hardness of the $k$-core minimization problem (KCM), including innaproximability and fixed-parameter intractability. Motivated by such a challenge in terms of algorithm design, we propose a novel algorithm inspired by Shapley value---a cooperative game-theoretic concept--- that is able to leverage the strong interdependencies in the effects of edge removals in the search space. As computing Shapley values is also NP-hard, we efficiently approximate them using a randomized algorithm with probabilistic guarantees. Our experiments, using several real datasets, show that the proposed algorithm outperforms competing solutions in terms of $k$-core minimization while being able to handle large graphs. Moreover, we illustrate how KCM can be applied in the analysis of the $k$-core resilience of networks.

Includes members:
  Member "towards scalable network delay minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximizing coverage centrality via network design extended version")
  Member "boosting information spread an algorithmic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")
  Member "social resilience in online communities the autopsy of friendster":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "preventing unraveling in social networks gets harder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building large k cores from sparse graphs")
  Member "parameterized exact and approximation algorithms for maximum k set cover and related satisfiability problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parameterised complexity of abduction in schaefer s framework")
  Member "graph measures and network robustness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the minimum edge compact spanner network design problem")
  Member "a model of internet topology using k shell decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "target privacy preserving for social networks")

Question: Please tell me which class the source paper 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.SI(Social and Information Networks)