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 "graph distance from the topological view of non backtracking cycles"):
  Abstract: Whether comparing networks to each other or to random expectation, measuring dissimilarity is essential to understanding the complex phenomena under study. However, determining the structural dissimilarity between networks is an ill-defined problem, as there is no canonical way to compare two networks. Indeed, many of the existing approaches for network comparison differ in their heuristics, efficiency, interpretability, and theoretical soundness. Thus, having a notion of distance that is built on theoretically robust first principles and that is interpretable with respect to features ubiquitous in complex networks would allow for a meaningful comparison between different networks. Here we introduce a theoretically sound and efficient new measure of graph distance, based on the "length spectrum" function from algebraic topology, which compares the structure of two undirected, unweighted graphs by considering their non-backtracking cycles. We show how this distance relates to structural features such as presence of hubs and triangles through the behavior of the eigenvalues of the so-called non-backtracking matrix, and we showcase its ability to discriminate between networks in both real and synthetic data sets. By taking a topological interpretation of non-backtracking cycles, this work presents a novel application of Topological Data Analysis to the study of complex networks.

Includes members:
  Member "an information theoretic all scales approach to comparing networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "on spectral graph embedding a non backtracking perspective and graph approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph embedding based hybrid social recommendation system")
  Member "the metric space of networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distances and isomorphism between networks and the stability of network invariants")
  Member "distances and isomorphism between networks and the stability of network invariants":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "motivic clustering schemes for directed graphs")
  Member "localization and centrality in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "an in depth analysis of stochastic kronecker graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances in scalable network generation")
  Member "deltacon a principled massive graph similarity function":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimating descriptors for large graphs")
  Member "spectral redemption in clustering sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)