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: "adaptive model for recommendation of news"
  Abstract: Most news recommender systems try to identify users' interests and news' attributes and use them to obtain recommendations. Here we propose an adaptive model which combines similarities in users' rating patterns with epidemic-like spreading of news on an evolving network. We study the model by computer agent-based simulations, measure its performance and discuss its robustness against bias and malicious behavior. Subject to the approval fraction of news recommended, the proposed model outperforms the widely adopted recommendation of news according to their absolute or relative popularity. This model provides a general social mechanism for recommender systems and may find its applications also in other types of 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 "centrality in modular networks"):
  with members:
    - "social centrality using network hierarchy and community structure"
    - "immunization of networks with non overlapping community structure"
    - "centrality measures for networks with community structure"
    - "community based immunization strategies for epidemic control"
    - "towards realistic artificial benchmark for community detection algorithms evaluation"
    - "the role of strong and weak ties in facebook a community structure perspective"
    - "social structure of facebook networks"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-3 (induced by source paper "diffusion like recommendation with enhanced similarity of objects"):
  with members:
    - "information filtering on coupled social networks"
    - "information filtering via balanced diffusion on bipartite networks"
    - "tag aware recommender systems a state of the art survey"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"

Connected through hyperedge HE-4 (induced by source paper "tag aware recommender systems a state of the art survey"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "social knowledge based recommender system application to the movies domain"
    - "empirical comparison of algorithms for network community detection"
    - "collaborative filtering with diffusion based similarity on tripartite graphs"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "personalized recommendation via integrated diffusion on user item tag tripartite graphs"
    - "collective dynamics of social annotation"
    - "collaborative tagging as a tripartite network"

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)