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 "gemsec graph embedding with self clustering"):
  Abstract: Modern graph embedding procedures can efficiently extract features of nodes from graphs with millions of nodes. The features are later used as inputs for downstream predictive tasks. In this paper we propose GEMSEC a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their features. The procedure places nodes in an abstract feature space where the vertex features minimize the negative log likelihood of preserving sampled vertex neighborhoods, while the nodes are clustered into a fixed number of groups in this space. GEMSEC is a general extension of earlier work in the domain as it is an augmentation of the core optimization problem of sequence based graph embedding procedures and is agnostic of the neighborhood sampling strategy. We show that GEMSEC extracts high quality clusters on real world social networks and is competitive with other community detection algorithms. We demonstrate that the clustering constraint has a positive effect on representation quality and also that our procedure learns to embed and cluster graphs jointly in a robust and scalable manner.

Includes members:
  Member "fast sequence based embedding with diffusion graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs")
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  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 "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "don t walk skip online learning of multi scale network embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs")
  Member "line large scale information network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")

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)