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: "evaluating social networks using task focused network inference"
  Abstract: Networks are representations of complex underlying social processes. However, the same given network may be more suitable to model one behavior of individuals than another. In many cases, aggregate population models may be more effective than modeling on the network. We present a general framework for evaluating the suitability of given networks for a set of predictive tasks of interest, compared against alternative, networks inferred from data. We present several interpretable network models and measures for our comparison. We apply this general framework to the case study on collective classification of music preferences in a newly available dataset of the this http URL social network.

Connected through hyperedge HE-1 (induced by source paper "network model selection for task focused attributed network inference"):
  with members:
    - "network structure inference a survey motivations methods and applications"
    - "the role of social networks in information diffusion"
    - "learning attitudes and attributes from multi aspect reviews"
    - "multiplicative attribute graph model of real world networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-2 (induced by source paper "network model selection using task focused minimum description length"):
  with members:
    - "network model selection for task focused attributed network inference"
    - "network structure inference a survey motivations methods and applications"

Connected through hyperedge HE-3 (induced by source paper "network structure inference a survey motivations methods and applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network model selection using task focused minimum description length"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "network model selection for task focused attributed network inference"
    - "on the efficiency of the information networks in social media"
    - "inferring networks of substitutable and complementary products"
    - "privacy in sensor driven human data collection a guide for practitioners"

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)