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: "carl content aware representation learning for heterogeneous networks"
  Abstract: Heterogeneous networks not only present a challenge of heterogeneity in the types of nodes and relations, but also the attributes and content associated with the nodes. While recent works have looked at representation learning on homogeneous and heterogeneous networks, there is no work that has collectively addressed the following challenges: (a) the heterogeneous structural information of the network consisting of multiple types of nodes and relations; (b) the unstructured semantic content (e.g., text) associated with nodes; and (c) online updates due to incoming new nodes in growing network. We address these challenges by developing a Content-Aware Representation Learning model (CARL). CARL performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Furthermore, an additional online update module is proposed for efficiently learning representations of incoming nodes. Extensive experiments demonstrate that CARL outperforms state-of-the-art baselines in various heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation and relevance search. We also demonstrate the effectiveness of the CARL's online update module through a category visualization study.

Connected through hyperedge HE-1 (induced by source paper "hetespaceywalk a heterogeneous spacey random walk for heterogeneous information network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "the spacey random walk a stochastic process for higher order data"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

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)