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 "is a single embedding enough learning node representations that capture multiple social contexts"):
  Abstract: Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph - a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to 90%. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure.

Includes members:
  Member "ddgk learning graph representations for deep divergence graph kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs")
  Member "enhanced network embeddings via exploiting edge labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "epine enhanced proximity information network embedding")
  Member "a tutorial on network embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels")
  Member "verse versatile graph embeddings from similarity measures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph ordering towards the optimal by learning")
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "harp hierarchical representation learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization")
  Member "learning edge representations via low rank asymmetric projections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning")
  Member "watch your step learning node embeddings via graph attention":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing")

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)