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: "on spectral graph embedding a non backtracking perspective and graph approximation"
  Abstract: Graph embedding has been proven to be efficient and effective in facilitating graph analysis. In this paper, we present a novel spectral framework called NOn-Backtracking Embedding (NOBE), which offers a new perspective that organizes graph data at a deep level by tracking the flow traversing on the edges with backtracking prohibited. Further, by analyzing the non-backtracking process, a technique called graph approximation is devised, which provides a channel to transform the spectral decomposition on an edge-to-edge matrix to that on a node-to-node matrix. Theoretical guarantees are provided by bounding the difference between the corresponding eigenvalues of the original graph and its graph approximation. Extensive experiments conducted on various real-world networks demonstrate the efficacy of our methods on both macroscopic and microscopic levels, including clustering and structural hole spanner detection.

Connected through hyperedge HE-1 (induced by source paper "graph embedding based hybrid social recommendation system"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "attributed network embedding for learning in a dynamic environment"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "hybrid recommender systems a systematic literature review"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (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"
    - "the metric space of networks"
    - "distances and isomorphism between networks and the stability of network invariants"
    - "localization and centrality in networks"
    - "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-3 (induced by source paper "fi grl fast inductive graph representation learning via projection cost preservation"):
  with members:
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "experiments with random projection"
    - "randomized dimensionality reduction for k means clustering"

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)