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: "mvn2vec preservation and collaboration in multi view network embedding"
  Abstract: Multi-view networks are ubiquitous in real-world applications. In order to extract knowledge or business value, it is of interest to transform such networks into representations that are easily machine-actionable. Meanwhile, network embedding has emerged as an effective approach to generate distributed network representations. Therefore, we are motivated to study the problem of multi-view network embedding, with a focus on the characteristics that are specific and important in embedding this type of networks. In our practice of embedding real-world multi-view networks, we identify two such characteristics, which we refer to as preservation and collaboration. We then explore the feasibility of achieving better embedding quality by simultaneously modeling preservation and collaboration, and propose the mvn2vec algorithms. With experiments on a series of synthetic datasets, an internal Snapchat dataset, and two public datasets, we further confirm the presence and importance of preservation and collaboration. These experiments also demonstrate that better embedding can be obtained by simultaneously modeling the two characteristics, while not over-complicating the model or requiring additional supervision.

Connected through hyperedge HE-1 (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"
    - "attributed social network embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-2 (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"
    - "an attention based collaboration framework for multi view network representation learning"
    - "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-3 (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"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an attention based collaboration framework for multi view network representation learning"
    - "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-4 (induced by source paper "unsupervised attributed multiplex network embedding"):
  with members:
    - "self attention graph pooling"
    - "lovasz convolutional networks"
    - "multi dimensional graph convolutional networks"
    - "mine mutual information neural estimation"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "multi view network embedding via graph factorization clustering and co regularized multi view agreement"):
  with members:
    - "learning from multi view multi way data via structural factorization machines"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "an attention based collaboration framework for multi view network representation learning"
    - "a survey on network embedding"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "a survey on multi view learning"

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)