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 "metric representations of networks a uniqueness result"):
  Abstract: In this paper, we consider the problem of projecting networks onto metric spaces. Networks are structures that encode relationships between pairs of elements or nodes. However, these relationships can be independent of each other, and need not be defined for every pair of nodes. This is in contrast to a metric space, which requires that a distance between every pair of elements in the space be defined. To understand how to project networks onto metric spaces, we take an axiomatic approach: we first state two axioms for projective maps from the set of all networks to the set of finite metric spaces, then show that only one projection satisfies these requirements. The developed technique is shown to be an effective method for finding approximate solutions to combinatorial optimization problems. Finally, we illustrate the use of metric trees for efficient search in projected networks.

Includes members:
  Member "a comprehensive survey of graph embedding problems techniques and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "hierarchical clustering of asymmetric networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flow smoothing and denoising graph signal processing in the edge space")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "admissible hierarchical clustering methods and algorithms for asymmetric networks":
    also connected through other hyperedges:
      (none)
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "axiomatic construction of hierarchical clustering in asymmetric networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "motivic clustering schemes for directed graphs")

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)