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: "dimensionality of social networks using motifs and eigenvalues"
  Abstract: We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.

Connected through hyperedge HE-1 (induced by source paper "dynamic competition networks detecting alliances and leaders"):
  with members:
    - "the spatial ecology of war and peace"
    - "pagerank beyond the web"
    - "negative link prediction in social media"
    - "models for on line social networks"

Connected through hyperedge HE-2 (induced by source paper "rumours spread slowly in a small world spatial network"):
  with members:
    - "average distance in a general class of scale free networks with underlying geometry"
    - "non uniform distribution of nodes in the spatial preferential attachment model"
    - "geometric graph properties of the spatial preferred attachment model"

Connected through hyperedge HE-3 (induced by source paper "mining and modeling character networks"):
  with members:
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"

Connected through hyperedge HE-4 (induced by source paper "the domination number of on line social networks and random geometric graphs"):
  with members:
    - "a scalable generative graph model with community structure"
    - "social structure of facebook networks"
    - "multiplicative attribute graph model of real world networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)