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: "a general framework for content enhanced network representation learning"
  Abstract: This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks. Most existing network embedding methods rely solely on the network structure, i.e., the linkage relationships between nodes, but ignore the rich content information associated with it, which is common in real world networks and beneficial to describing the characteristics of a node. In this paper, we propose content-enhanced network embedding (CENE), which is capable of jointly leveraging the network structure and the content information. Our approach integrates text modeling and structure modeling in a general framework by treating the content information as a special kind of node. Experiments on several real world net- works with application to node classification show that our models outperform all existing network embedding methods, demonstrating the merits of content information and joint learning.

Connected through hyperedge HE-1 (induced by source paper "graph neighborhood attentive pooling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diffusion maps for textual network embedding"
    - "attentive pooling networks"

Connected through hyperedge HE-2 (induced by source paper "deep graph representation learning"):
  with members:
    - "learning representations of graph data a survey"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "large scale learnable graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "predicting multicellular function through multi layer tissue networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "pytorch biggraph a large scale graph embedding system"
    - "network structure from rich but noisy data"
    - "understanding convolutional neural networks for text classification"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "interpretable vaes for nonlinear group factor analysis"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-5 (induced by source paper "dynamic embedding on textual networks via a gaussian process"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "dyngem deep embedding method for dynamic graphs"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "diffusion maps for textual network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning over dynamic graphs"

Connected through hyperedge HE-6 (induced by source paper "a deep neural information fusion architecture for textual network embeddings"):
  with members:
    - "sac2vec information network representation with structure and content"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "language modeling with gated convolutional networks"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "embedding biomedical ontologies by jointly encoding network structure and textual node descriptors"):
  with members:
    - "abusive language detection with graph convolutional networks"
    - "deep relevance ranking using enhanced document query interactions"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "node2vec scalable feature learning for networks"
    - "bag of tricks for efficient text classification"
    - "deep residual learning for image recognition"
    - "line large scale information network embedding"

Connected through hyperedge HE-8 (induced by source paper "tag2vec learning tag representations in tag networks"):
  with members:
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for meta path based proximity"
    - "poincar e embeddings for learning hierarchical representations"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

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)