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 "capturing edge attributes via network embedding"):
  Abstract: Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction, and node classification. Most existing embedding methods rely solely on network structure. However, in practice, we often have auxiliary information about the nodes and/or their interactions, e.g., the content of scientific papers in coauthorship networks, or topics of communication in Twitter mention networks. Here, we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations. Our method jointly minimizes the reconstruction error for higher order node neighborhood, social roles, and edge attributes using a deep architecture that can adequately capture highly nonlinear interactions. We demonstrate the efficacy of our model over existing state-of-the-art methods on a variety of real-world networks including collaboration networks and social networks. We also observe that using edge attributes to inform network embedding yields better performance in downstream tasks such as link prediction and node classification.

Includes members:
  Member "dyngraph2vec capturing network dynamics using dynamic graph representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "dyngem deep embedding method for dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inductive representation learning on temporal graphs")
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "disinformation and social bot operations in the run up to the 2017 french presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "graph2vec learning distributed representations of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "scalable temporal latent space inference for link prediction in dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation learning")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep graph matching consensus")

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)