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: "an attention based collaboration framework for multi view network representation learning"
  Abstract: Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.

Connected through hyperedge HE-1 (induced by source paper "aligraph a comprehensive graph neural network platform"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph networks as learnable physics engines for inference and control"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "attributed social network embedding"
    - "one shot relational learning for knowledge graphs"
    - "struc2vec learning node representations from structural identity"
    - "principled multilayer network embedding"

Connected through hyperedge HE-2 (induced by source paper "representation learning for attributed multiplex heterogeneous network"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "attributed social network embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-3 (induced by source paper "task guided pair embedding in heterogeneous network"):
  with members:
    - "link prediction based on graph neural networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "capturing edge attributes via network embedding"
    - "advances in pre training distributed word representations"
    - "heterogeneous information network embedding for recommendation"
    - "effectiveness of anonymization in double blind review"

Connected through hyperedge HE-4 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "representation learning for attributed multiplex heterogeneous network"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"
    - "a unified framework for community detection and network representation learning"

Connected through hyperedge HE-5 (induced by source paper "bayes embedding bem refining representation by integrating knowledge graphs and behavior specific networks"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "billion scale similarity search with gpus"
    - "a survey on network embedding"
    - "learning combinatorial optimization algorithms over graphs"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-6 (induced by source paper "compositional network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "relational inductive biases deep learning and graph networks"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "node2vec scalable feature learning for networks"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "unsupervised attributed multiplex network embedding"):
  with members:
    - "self attention graph pooling"
    - "lovasz convolutional networks"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "multi dimensional graph convolutional networks"
    - "mine mutual information neural estimation"
    - "struc2vec learning node representations from structural identity"
    - "principled multilayer network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-8 (induced by source paper "representation learning in heterogeneous professional social networks with ambiguous social connections"):
  with members:
    - "attributed social network embedding"
    - "representation learning on graphs methods and applications"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "node2vec scalable feature learning for networks"
    - "proje embedding projection for knowledge graph completion"
    - "a survey of heterogeneous information network analysis"

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)