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 "hyperbolic multiplex network embedding with maps of random walk"):
  Abstract: Recent research on network embedding in hyperbolic space have proven successful in several applications. However, nodes in real world networks tend to interact through several distinct channels. Simple aggregation or ignorance of this multiplexity will lead to misleading results. On the other hand, there exists redundant information between different interaction patterns between nodes. Recent research reveals the analogy between the community structure and the hyperbolic coordinate. To learn each node's effective embedding representation while reducing the redundancy of multiplex network, we then propose a unified framework combing multiplex network hyperbolic embedding and multiplex community detection. The intuitive rationale is that high order node embedding approach is expected to alleviate the observed network's sparse and noisy structure which will benefit the community detection task. On the contrary, the improved community structure will also guide the node embedding task. To incorporate the common features between channels while preserving unique features, a random walk approach which traversing in latent multiplex hyperbolic space is proposed to detect the community across channels and bridge the connection between node embedding and community detection. The proposed framework is evaluated on several network tasks using different real world dataset. The results demonstrates that our framework is effective and efficiency compared with state-of-the-art approaches.

Includes members:
  Member "multi relational poincar e graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion")
  Member "representation learning for attributed multiplex heterogeneous network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "magnn metapath aggregated graph neural network for heterogeneous graph embedding")
  Member "mvn2vec preservation and collaboration in multi view network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "representation learning for attributed multiplex heterogeneous network")
  Member "an attention based collaboration framework for multi view network representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aligraph a comprehensive graph neural network platform")
  Member "principled multilayer network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aligraph a comprehensive graph neural network platform")
  Member "a survey on network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "poincar e embeddings for learning hierarchical representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the tensor brain semantic decoding for perception and memory")
  Member "representation learning for scale free networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "network2vec learning node representation based on space mapping in networks")

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)