human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "adversarial deep network embedding for cross network node classification"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "network together node classification via cross network deep network embedding"
    - "network transfer learning via adversarial domain adaptation with graph convolution"
    - "label efficient semi supervised learning via graph filtering"
    - "regal representation learning based graph alignment"
    - "adversarial discriminative domain adaptation"
    - "adversarial network embedding"

Connected through hyperedge HE-2 (induced by source paper "network transfer learning via adversarial domain adaptation with graph convolution"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "beyond sharing weights for deep domain adaptation"
    - "attributed graph clustering via adaptive graph convolution"
    - "graph neural networks for social recommendation"
    - "label efficient semi supervised learning via graph filtering"
    - "regal representation learning based graph alignment"
    - "deep visual domain adaptation a survey"

Question: Please tell me which class the center node 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)