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 representation learning a survey"
  Abstract: With the widespread use of information technologies, information networks have increasingly become popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of society, information diffusion, and different patterns of communication. However, the large scale of information networks often makes network analytic tasks computationally expensive and intractable. Recently, network representation learning has been proposed as a new learning paradigm that embeds network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a thorough review of the current literature on network representation learning in the field of data mining and machine learning. We propose a new categorization to analyze and summarize state-of-the-art network representation learning techniques according to the methodology they employ and the network information they preserve. Finally, to facilitate research on this topic, we summarize benchmark datasets and evaluation methodologies, and discuss open issues and future research directions in this field.

Connected through hyperedge HE-1 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "community aware random walk for network embedding"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "dynamic graph representation learning via self attention networks"
    - "attributed social network embedding"
    - "a survey on network embedding"

Connected through hyperedge HE-2 (induced by source paper "epine enhanced proximity information network embedding"):
  with members:
    - "enhanced network embeddings via exploiting edge labels"
    - "node2vec scalable feature learning for networks"
    - "don t walk skip online learning of multi scale network embeddings"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"

Connected through hyperedge HE-4 (induced by source paper "a survey and taxonomy of adversarial neural networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "how generative adversarial networks and their variants work an overview"
    - "mirrorgan learning text to image generation by redescription"
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "adversarial learning of semantic relevance in text to image synthesis"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-5 (induced by source paper "from community to role based graph embeddings"):
  with members:
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "overlapping community detection with graph neural networks"
    - "feature propagation on graph a new perspective to graph representation learning"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "higher order graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank"):
  with members:
    - "adversarial training methods for network embedding"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "efficient algorithms for approximate single source personalized pagerank queries"
    - "pytorch biggraph a large scale graph embedding system"
    - "scalable graph embeddings via sparse transpose proximities"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"

Connected through hyperedge HE-7 (induced by source paper "learning graph embedding with adversarial training methods"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "adversarial text generation via feature mover s distance"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "stochastic answer networks for natural language inference"

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)