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: "is a single vector enough exploring node polysemy for network embedding"
  Abstract: Networks have been widely used as the data structure for abstracting real-world systems as well as organizing the relations among entities. Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction. Existing network embedding models comprehensively integrate all information of each node, such as links and attributes, towards a single embedding vector to represent the node's general role in the network. However, a real-world entity could be multifaceted, where it connects to different neighborhoods due to different motives or self-characteristics that are not necessarily correlated. For example, in a movie recommender system, a user may love comedies or horror movies simultaneously, but it is not likely that these two types of movies are mutually close in the embedding space, nor the user embedding vector could be sufficiently close to them at the same time. In this paper, we propose a polysemous embedding approach for modeling multiple facets of nodes, as motivated by the phenomenon of word polysemy in language modeling. Each facet of a node is mapped to an embedding vector, while we also maintain an association degree between each pair of node and facet. The proposed method is adaptive to various existing embedding models, without significantly complicating the optimization process. We also discuss how to engage embedding vectors of different facets for inference tasks including classification and link prediction. Experiments on real-world datasets help comprehensively evaluate the performance of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "learning to hash with graph neural networks for recommender systems"):
  with members:
    - "deep representation learning for social network analysis"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-2 (induced by source paper "deep structured cross modal anomaly detection"):
  with members:
    - "specae spectral autoencoder for anomaly detection in attributed networks"
    - "learning two branch neural networks for image text matching tasks"
    - "deep structured energy based models for anomaly detection"
    - "learning deep structure preserving image text embeddings"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-3 (induced by source paper "specae spectral autoencoder for anomaly detection in attributed networks"):
  with members:
    - "deep structured cross modal anomaly detection"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-4 (induced by source paper "pyodds an end to end outlier detection system"):
  with members:
    - "deep structured cross modal anomaly detection"
    - "specae spectral autoencoder for anomaly detection in attributed networks"
    - "pyod a python toolbox for scalable outlier detection"
    - "lstm based encoder decoder for multi sensor anomaly detection"

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)