human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "partitioning networks with node attributes by compressing information flow"):
  Abstract: Real-world networks are often organized as modules or communities of similar nodes that serve as functional units. These networks are also rich in content, with nodes having distinguishing features or attributes. In order to discover a network's modular structure, it is necessary to take into account not only its links but also node attributes. We describe an information-theoretic method that identifies modules by compressing descriptions of information flow on a network. Our formulation introduces node content into the description of information flow, which we then minimize to discover groups of nodes with similar attributes that also tend to trap the flow of information. The method has several advantages: it is conceptually simple and does not require ad-hoc parameters to specify the number of modules or to control the relative contribution of links and node attributes to network structure. We apply the proposed method to partition real-world networks with known community structure. We demonstrate that adding node attributes helps recover the underlying community structure in content-rich networks more effectively than using links alone. In addition, we show that our method is faster and more accurate than alternative state-of-the-art algorithms.

Includes members:
  Member "scalable text and link analysis with mixed topic link models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic blockmodels with edge information")
  Member "spectral clustering with epidemic diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the impact of network flows on community formation in models of opinion dynamics")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "mining attribute structure correlated patterns in large attributed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach")
  Member "image labeling on a network using social network metadata for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "efficient community detection in large networks using content and links":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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)