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 "when engagement meets similarity efficient k r core computation on social networks"):
  Abstract: In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we also consider the pairwise similarity between users based on their profiles. For a given similarity metric and a similarity threshold r, the similarity between any two vertices in a (k,r)-core is ensured not less than r. Efficient algorithms are proposed to enumerate all maximal (k,r)-cores and find the maximum (k,r)-core, where both problems are shown to be NP-hard. Effective pruning techniques significantly reduce the search space of two algorithms and a novel (k,k')-core based (k,r)-core size upper bound enhances performance of the maximum (k,r)-core computation. We also devise effective search orders to accommodate the different nature of two mining algorithms. Comprehensive experiments on real-life data demonstrate that the maximal/maximum (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of two mining algorithms is significantly improved by proposed techniques.

Includes members:
  Member "i o efficient core graph decomposition at web scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "critical edge identification a k truss based model")
  Member "towards realistic team formation in social networks based on densest subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping")
  Member "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sensing and modeling human behavior using social media and mobile data")
  Member "locally estimating core numbers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local algorithms for hierarchical dense subgraph discovery")
  Member "geo social group queries with minimum acquaintance constraint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "maximizing friend making likelihood for social activity organization")
  Member "preventing unraveling in social networks gets harder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "building large k cores from sparse graphs")
  Member "parameterized complexity of the anchored k core problem for directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "building large k cores from sparse graphs")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding temporal patterns using algebraic fingerprints")

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)