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: "identifying influential nodes in bipartite networks using the clustering coefficient"
  Abstract: The identification of influential nodes in complex network can be very challenging. If the network has a community structure, centrality measures may fail to identify the complete set of influential nodes, as the hubs and other central nodes of the network may lie inside only one community. Here we define a bipartite clustering coefficient that, by taking differently structured clusters into account, can find important nodes across communities.

Connected through hyperedge HE-1 (induced by source paper "hellrank a hellinger based centrality measure for bipartite social networks"):
  with members:
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "user recommendations in reciprocal and bipartite social networks an online dating case study"
    - "graph cluster randomization network exposure to multiple universes"
    - "network sampling from static to streaming graphs"
    - "benefits of bias towards better characterization of network sampling"

Connected through hyperedge HE-2 (induced by source paper "fast extraction of the backbone of projected bipartite networks to aid community detection"):
  with members:
    - "predicting item popularity analysing local clustering behaviour of users"
    - "extracting the information backbone in online system"

Connected through hyperedge HE-3 (induced by source paper "predicting item popularity analysing local clustering behaviour of users"):
  with members:
    - "trend prediction in temporal bipartite networks the case of movielens netflix and digg"

Connected through hyperedge HE-4 (induced by source paper "ranking the importance level of intermediaries to a criminal using a reliance measure"):
  with members:
    - "using shortest path to discover criminal community"
    - "identifying a criminal s network of trust"
    - "forensic analysis of phone call networks"
    - "identifying high betweenness centrality nodes in large social networks"
    - "a novel measure of edge centrality in social 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)