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: "explainable entity based recommendations with knowledge graphs"
  Abstract: Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is unavailable, generating explanations is still a hard problem. In this paper, we illustrate how explanations can be generated in such a scenario by leveraging external knowledge in the form of knowledge graphs. Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.

Connected through hyperedge HE-1 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "jointly learning explainable rules for recommendation with knowledge graph"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "deep reinforcement learning for page wise recommendations"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "outer product based neural collaborative filtering"
    - "explainable recommendation a survey and new perspectives"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "conceptualize and infer user needs in e commerce"):
  with members:
    - "deep session interest network for click through rate prediction"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation a survey and new perspectives"
    - "deep cascade multi task learning for slot filling in online shopping assistant"
    - "attention is all you need"
    - "neural rating regression with abstractive tips generation for recommendation"

Connected through hyperedge HE-4 (induced by source paper "distilling structured knowledge into embeddings for explainable and accurate recommendation"):
  with members:
    - "neural ir meets graph embedding a ranking model for product search"
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "deep learning based recommender system a survey and new perspectives"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable recommendation a survey and new perspectives"
    - "neural factorization machines for sparse predictive analytics"
    - "deep mutual learning"
    - "neural collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "towards combinational relation linking over knowledge graphs"):
  with members:
    - "graph pattern entity ranking model for knowledge graph completion"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "toruse knowledge graph embedding on a lie group"
    - "modeling relation paths for representation learning of knowledge bases"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-6 (induced by source paper "explainable recommendation a survey and new perspectives"):
  with members:
    - "attention is not not explanation"
    - "the fact taming latent factor models for explainability with factorization trees"
    - "learning disentangled representations for recommendation"
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "attention is not explanation"
    - "incorporating interpretability into latent factor models via fast influence analysis"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "mmalfm explainable recommendation by leveraging reviews and images"

Connected through hyperedge HE-7 (induced by source paper "learning heterogeneous knowledge base embeddings for explainable recommendation"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "translation based recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "max margin nonparametric latent feature models for link prediction"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "learning over knowledge base embeddings for recommendation"):
  with members:
    - "translation based recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

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)