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: "extracting hidden groups and their structure from streaming interaction data"
  Abstract: When actors in a social network interact, it usually means they have some general goal towards which they are collaborating. This could be a research collaboration in a company or a foursome planning a golf game. We call such groups planning groups. In many social contexts, it might be possible to observe the dyadic interactions between actors, even if the actors do not explicitly declare what groups they belong too. When groups are not explicitly declared, we call them hidden groups. Our particular focus is hidden planning groups. By virtue of their need to further their goal, the actors within such groups must interact in a manner which differentiates their communications from random background communications. In such a case, one can infer (from these interactions) the composition and structure of the hidden planning groups. We formulate the problem of hidden group discovery from streaming interaction data, and we propose efficient algorithms for identifying the hidden group s by isolating the hidden group’s non-random, planning-related, communications from the random background communications. We validate our algorithms on real data (the Enron email corpus and Blog communication data). Analysis of the results reveals that our algorithms extract meaningful hidden group structures.

Connected through hyperedge HE-1 (induced by source paper "are you going to the party depends who else is coming learning hidden group dynamics via conditional latent tree models"):
  with members:
    - "learning the latent state space of time varying graphs"
    - "a tensor approach to learning mixed membership community models"
    - "community detection in networks with node attributes"
    - "efficient active algorithms for hierarchical clustering"

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)