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: "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.

Connected through hyperedge HE-1 (induced by source paper "fast algorithms for intimate core group search in weighted graphs"):
  with members:
    - "a survey of community search over big graphs"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "the minimum wiener connector problem"
    - "approximate closest community search in networks"
    - "truss decomposition in massive networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-2 (induced by source paper "manipulating node similarity measures in network"):
  with members:
    - "covert networks how hard is it to hide"
    - "influence minimization under budget and matroid constraints extended version"
    - "attack tolerance of link prediction algorithms how to hide your relations in a social network"
    - "target defense against link prediction based attacks via evolutionary perturbations"
    - "link prediction in social networks the state of the art"

Connected through hyperedge HE-3 (induced by source paper "k core maximization through edge additions"):
  with members:
    - "a fast order based approach for core maintenance"
    - "preventing unraveling in social networks gets harder"
    - "an o m algorithm for cores decomposition of networks"

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.SI(Social and Information Networks)