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: "multi modal bayesian embeddings for learning social knowledge graphs"
  Abstract: We study the extent to which online social networks can be connected to open knowledge bases. The problem is referred to as learning social knowledge graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings. GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities---i.e., social network users and knowledge concepts---in a shared latent topic space. Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods. We then deploy the method on AMiner, a large-scale online academic search system with a network of 38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our method significantly decreases the error rate in an online A/B test with live users.

Connected through hyperedge HE-1 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "mile a multi level framework for scalable graph embedding"

Connected through hyperedge HE-2 (induced by source paper "graph embedding techniques applications and performance a survey"):
  with members:
    - "harp hierarchical representation learning for networks"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "walklets multiscale graph embeddings for interpretable network classification"

Connected through hyperedge HE-3 (induced by source paper "network embedding as matrix factorization unifying deepwalk line pte and node2vec"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "a latent variable model approach to pmi based word embeddings"
    - "comprehend deepwalk as matrix factorization"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-4 (induced by source paper "a tutorial on network embeddings"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "sne signed network embedding"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "a neural network approach to joint modeling social networks and mobile trajectories"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive survey of graph embedding problems techniques and applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "geometric deep learning going beyond euclidean data"
    - "graph based isometry invariant representation learning"
    - "topological recurrent neural network for diffusion prediction"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "on the representation and embedding of knowledge bases beyond binary relations"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"

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.CL(Computation and Language)