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: "modeling social networks with node attributes using the multiplicative attribute graph model"
  Abstract: Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the probability of an edge as the product of individual attribute link formation affinities. We develop a scalable variational expectation maximization parameter estimation method. Experiments show that MAG model reliably captures network connectivity as well as provides insights into how different attributes shape the network structure.

Connected through hyperedge HE-1 (induced by source paper "node isolation in large homogeneous binary multiplicative attribute graph models"):
  with members:
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-2 (induced by source paper "detecting strong ties using network motifs"):
  with members:
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "triadic measures on graphs the power of wedge sampling"
    - "spectral redemption in clustering sparse networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-3 (induced by source paper "accelerating dependency graph learning from heterogeneous categorical event streams via knowledge transfer"):
  with members:
    - "transfer learning across networks for collective classification"

Connected through hyperedge HE-4 (induced by source paper "latent space model for multi modal social data"):
  with members:
    - "structure and inference in annotated networks"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "on facebook most ties are weak"
    - "online popularity and topical interests through the lens of instagram"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "traveling trends social butterflies or frequent fliers"

Connected through hyperedge HE-5 (induced by source paper "snap a general purpose network analysis and graph mining library"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "ringo interactive graph analytics on big memory machines"
    - "network lasso clustering and optimization in large graphs"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "nonparametric multi group membership model for dynamic networks"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego networks"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-6 (induced by source paper "estimating latent feature feature interactions in large feature rich graphs"):
  with members:
    - "evaluating link prediction methods"
    - "a network model characterized by a latent attribute structure with competition"
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-7 (induced by source paper "modeling transitivity in complex networks"):
  with members:
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-8 (induced by source paper "nonparametric multi group membership model for dynamic networks"):
  with members:
    - "latent multi group membership graph model"
    - "an infinite latent attribute model for network data"
    - "infinite multiple membership relational modeling for complex networks"
    - "multiplicative attribute graph model of real world networks"
    - "supervised random walks predicting and recommending links 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)