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: "item silk road recommending items from information domains to social users"
  Abstract: Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before.   Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "neural graph collaborative filtering"
    - "adversarial sampling and training for semi supervised information retrieval"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "price aware recommendation with graph convolutional networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "how powerful are graph neural networks"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "fast matrix factorization with non uniform weights on missing data"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "outer product based neural collaborative filtering"
    - "adversarial personalized ranking for recommendation"
    - "a simple convolutional generative network for next item recommendation"

Connected through hyperedge HE-5 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-6 (induced by source paper "tripartite heterogeneous graph propagation for large scale social recommendation"):
  with members:
    - "graph neural networks for social recommendation"
    - "representation learning for attributed multiplex heterogeneous network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "applying deep learning to airbnb search"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-7 (induced by source paper "kgat knowledge graph attention network for recommendation"):
  with members:
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "neural graph collaborative filtering"
    - "neural collective entity linking"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-8 (induced by source paper "parallel segregation integration networks for shared account cross domain sequential recommendations"):
  with members:
    - "sequence aware recommender systems"
    - "conet collaborative cross networks for cross domain recommendation"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "cross domain recommendation via deep domain adaptation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural 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.IR(Information Retrieval)