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: "deep network embedding for graph representation learning in signed networks"
  Abstract: Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network. The vast majority of existing network embedding algorithms, however, are only designed for unsigned networks, and the signed networks containing both positive and negative links, have pretty distinct properties from the unsigned counterpart. In this paper, we propose a deep network embedding model to learn the low-dimensional node vector representations with structural balance preservation for the signed networks. The model employs a semisupervised stacked auto-encoder to reconstruct the adjacency connections of a given signed network. As the adjacency connections are overwhelmingly positive in the real-world signed networks, we impose a larger penalty to make the auto-encoder focus more on reconstructing the scarce negative links than the abundant positive links. In addition, to preserve the structural balance property of signed networks, we design the pairwise constraints to make the positively connected nodes much closer than the negatively connected nodes in the embedding space. Based on the network representations learned by the proposed model, we conduct link sign prediction and community detection in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks.

Connected through hyperedge HE-1 (induced by source paper "adversarial deep network embedding for cross network node classification"):
  with members:
    - "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"
    - "network embedding for cross network node classification"
    - "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:
    - "adversarial training methods for network embedding"
    - "beyond sharing weights for deep domain adaptation"
    - "attributed graph clustering via adaptive graph convolution"
    - "network embedding for cross network node classification"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning graph embedding with adversarial training methods"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "attributed social network embedding"
    - "semi supervised learning on graphs with generative adversarial nets"
    - "network representation learning a survey"

Connected through hyperedge HE-4 (induced by source paper "network together node classification via cross network deep network embedding"):
  with members:
    - "adversarial training methods for network embedding"
    - "regal representation learning based graph alignment"
    - "struc2vec learning node representations from structural identity"
    - "adversarial network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "adversarial training methods for network embedding"):
  with members:
    - "adversarial attack on graph structured data"
    - "adversarial personalized ranking for recommendation"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "adversarial network embedding"
    - "node2vec scalable feature learning for networks"
    - "improved techniques for training gans"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "network embedding for cross network node classification"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "transfer learning across networks for collective classification"
    - "a kernel method for the two sample problem"

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)