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 "a survey on network embedding"):
  Abstract: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.

Includes members:
  Member "fast warped graph embedding unifying framework and one click algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving textual network learning with variational homophilic embeddings")
  Member "heterogeneous information network embedding for meta path based proximity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep representation learning for social network analysis")
  Member "bibliographic analysis with the citation network topic model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attributed social network embedding")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "a general framework for content enhanced network representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph neighborhood attentive pooling")
  Member "end to end joint learning of natural language understanding and dialogue manager":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plato dialogue system a flexible conversational ai research platform")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")

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)