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: "sac2vec information network representation with structure and content"
  Abstract: Network representation learning (also known as information network embedding) has been the central piece of research in social and information network analysis for the last couple of years. An information network can be viewed as a linked structure of a set of entities. A set of linked web pages and documents, a set of users in a social network are common examples of information network. Network embedding learns low dimensional representations of the nodes, which can further be used for downstream network mining applications such as community detection or node clustering. Information network representation techniques traditionally use only the link structure of the network. But in real world networks, nodes come with additional content such as textual descriptions or associated images. This content is semantically correlated with the network structure and hence using the content along with the topological structure of the network can facilitate the overall network representation. In this paper, we propose Sac2Vec, a network representation technique that exploits both the structure and content. We convert the network into a multi-layered graph and use random walk and language modeling technique to generate the embedding of the nodes. Our approach is simple and computationally fast, yet able to use the content as a complement to structure and vice-versa. We also generalize the approach for networks having multiple types of content in each node. Experimental evaluations on four real world publicly available datasets show the merit of our approach compared to state-of-the-art algorithms in the domain.

Connected through hyperedge HE-1 (induced by source paper "a deep neural information fusion architecture for textual network embeddings"):
  with members:
    - "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"
    - "a general framework for content enhanced network representation learning"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

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)