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: "localization and centrality in networks"
  Abstract: Eigenvector centrality is a common measure of the importance of nodes in a network. Here we show that under common conditions the eigenvector centrality displays a localization transition that causes most of the weight of the centrality to concentrate on a small number of nodes in the network. In this regime the measure is no longer useful for distinguishing among the remaining nodes and its efficacy as a network metric is impaired. As a remedy, we propose an alternative centrality measure based on the nonbacktracking matrix, which gives results closely similar to the standard eigenvector centrality in dense networks where the latter is well behaved, but avoids localization and gives useful results in regimes where the standard centrality fails.

Connected through hyperedge HE-1 (induced by source paper "factorized graph representations for semi supervised learning from sparse data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "a general framework for estimating graphlet statistics via random walk"
    - "semi supervised classification with graph convolutional networks"
    - "graph based semi supervised learning for relational networks"
    - "semi supervised learning with heterophily"
    - "linearized and single pass belief propagation"
    - "spectral redemption in clustering sparse networks"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-2 (induced by source paper "nonlocal pagerank"):
  with members:
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "three hypergraph eigenvector centralities"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "the spacey random walk a stochastic process for higher order data"
    - "higher order organization of complex networks"
    - "total communicability as a centrality measure"

Connected through hyperedge HE-3 (induced by source paper "node immunization with non backtracking eigenvalues"):
  with members:
    - "graph comparison via the nonbacktracking spectrum"
    - "assessing percolation threshold based on high order non backtracking matrices"
    - "a scalable generative graph model with community structure"
    - "spectral redemption in clustering sparse networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "change detection in noisy dynamic networks a spectral embedding approach"):
  with members:
    - "change detection in dynamic attributed networks"
    - "a probabilistic framework to node level anomaly detection in communication networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "link prediction for interdisciplinary collaboration via co authorship network"
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "fast change point detection on dynamic social networks"
    - "detection and localization of change points in temporal networks with the aid of stochastic block models"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-5 (induced by source paper "spectral density of equitable core periphery graphs"):
  with members:
    - "spectral partitioning in equitable graphs"
    - "centrality metrics and localization in core periphery networks"
    - "identification of core periphery structure in networks"
    - "core periphery structure in networks"

Connected through hyperedge HE-6 (induced by source paper "spectra of networks containing short loops"):
  with members:
    - "graph spectra and the detectability of community structure in networks"
    - "localization on low order eigenvectors of data matrices"

Connected through hyperedge HE-7 (induced by source paper "graph distance from the topological view of non backtracking cycles"):
  with members:
    - "an information theoretic all scales approach to comparing networks"
    - "on spectral graph embedding a non backtracking perspective and graph approximation"
    - "the metric space of networks"
    - "distances and isomorphism between networks and the stability of network invariants"
    - "an in depth analysis of stochastic kronecker graphs"
    - "deltacon a principled massive graph similarity function"
    - "spectral redemption in clustering sparse networks"
    - "network sampling from static to streaming graphs"

Connected through hyperedge HE-8 (induced by source paper "non backtracking centrality based random walk on networks"):
  with members:
    - "assessing percolation threshold based on high order non backtracking matrices"
    - "the entropy of conditional markov trajectories"
    - "spectral redemption in clustering sparse networks"
    - "estimating and sampling graphs with multidimensional random walks"

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)