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: "google matrix of twitter"
  Abstract: We construct the Google matrix of the entire Twitter network, dated by July 2009, and analyze its spectrum and eigenstate properties including the PageRank and CheiRank vectors and 2DRanking of all nodes. Our studies show much stronger inter-connectivity between top PageRank nodes for the Twitter network compared to the networks of Wikipedia and British Universities studied previously. Our analysis allows to locate the top Twitter users which control the information flow on the network. We argue that this small fraction of the whole number of users, which can be viewed as the social network elite, plays the dominant role in the process of opinion formation on the network.

Connected through hyperedge HE-1 (induced by source paper "spectral properties of google matrix of wikipedia and other networks"):
  with members:
    - "toward two dimensional search engines"
    - "fractal weyl law for linux kernel architecture"
    - "towards physical laws for software architecture"
    - "two dimensional ranking of wikipedia articles"
    - "spectral properties of the google matrix of the world wide web and other directed networks"
    - "delocalization transition for the google matrix"

Connected through hyperedge HE-2 (induced by source paper "limited attention and centrality in social networks"):
  with members:
    - "rethinking centrality the role of dynamical processes in social network analysis"
    - "parameterized centrality metric for network analysis"
    - "non conservative diffusion and its application to social network analysis"
    - "predicting influential users in online social networks"
    - "crowdsourcing attention and productivity"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "the geospatial characteristics of a social movement communication network"):
  with members:
    - "dynamical classes of collective attention in twitter"
    - "twitter mood predicts the stock market"
    - "social networks that matter twitter under the microscope"

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)