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 "network embedding for cross network node classification"):
  Abstract: Network embedding is an effective method to learn low-dimensional node vector representations which can well preserve the original network structures. Existing network embedding algorithms are mostly developed for a single network, which fail to learn generalized and comparable feature representations across networks. In this paper, we address a cross-network node classification problem by leveraging the abundant labeled information from a source network to help classify unlabeled nodes in a target network. A cross-network deep network embedding (CDNE) model is proposed to embed the nodes from the source network and the target network into a unified low-dimensional latent space. This model integrates deep network embedding and domain adaptation to learn label-discriminative and network-invariant node vector representations. The network structures, node attributes and node labels are leveraged collectively to learn similar hidden vector representations for similar nodes within a network and across different networks. Extensive experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art related algorithms for node classification in the target network.

Includes members:
  Member "deep network embedding for graph representation learning in signed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial deep network embedding for cross network node classification")
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "semi supervised embedding in attributed networks with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "network representation learning consolidation and renewed bearing")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep graph matching consensus")
  Member "line large scale information network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "transfer learning across networks for collective classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial deep network embedding for cross network node classification")
  Member "a kernel method for the two sample problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")

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)