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: "personalized news recommendation with context trees"
  Abstract: The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with similar content, and frequently we have insufficient information to profile the reader.   In this paper, we introduce a class of news recommendation systems based on context trees. They can provide high-quality news recommendations to anonymous visitors based on present browsing behaviour. Using an unbiased testing methodology, we show that they make accurate and novel recommendations, and that they are sufficiently flexible for the challenges of news recommendation.

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 "contextual hybrid session based news recommendation with recurrent neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "news session based recommendations using deep neural networks"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "neural attentive session based recommendation"

Connected through hyperedge HE-3 (induced by source paper "sequential recommender systems challenges progress and prospects"):
  with members:
    - "session based recommendation with graph neural network"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "a survey on session based recommender systems"
    - "recommendation through mixtures of heterogeneous item relationships"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"

Connected through hyperedge HE-4 (induced by source paper "neighborhood enhanced and time aware model for session based recommendation"):
  with members:
    - "evaluation of session based recommendation algorithms"
    - "attention is all you need"
    - "neural attentive session based recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "content based news recommendation via shortest entity distance over knowledge graphs"):
  with members:
    - "neural networks models for entity discovery and linking"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-6 (induced by source paper "sequence aware recommender systems"):
  with members:
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "personalized course sequence recommendations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "learning distributed representations from reviews for collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "evaluation of session based recommendation algorithms"):
  with members:
    - "sequence aware recommender systems"
    - "learning from history and present next item recommendation via discriminatively exploiting user behaviors"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "context tree for adaptive session based recommendation"):
  with members:
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "recurrent latent variable networks for session based recommendation"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

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)