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 recommendation via multi task learning in opinionated text data"
  Abstract: Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.

Connected through hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "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"
    - "aurora auditing pagerank on large graphs"

Connected through hyperedge HE-2 (induced by source paper "explainable product search with a dynamic relation embedding model"):
  with members:
    - "conversational product search based on negative feedback"
    - "leverage implicit feedback for context aware product search"
    - "a zero attention model for personalized product search"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"

Connected through hyperedge HE-3 (induced by source paper "explainable knowledge graph based recommendation via deep reinforcement learning"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "session based social recommendation via dynamic graph attention networks"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation a survey and new perspectives"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-4 (induced by source paper "a capsule network for recommendation and explaining what you like and dislike"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "multi pointer co attention networks for recommendation"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "zero shot user intent detection via capsule neural networks"
    - "neural factorization machines for sparse predictive analytics"
    - "dynamic routing between capsules"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-5 (induced by source paper "incorporating interpretability into latent factor models via fast influence analysis"):
  with members:
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "outer product based neural collaborative filtering"
    - "explainable recommendation a survey and new perspectives"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "scalable and interpretable product recommendations via overlapping co clustering"

Connected through hyperedge HE-6 (induced by source paper "the fact taming latent factor models for explainability with factorization trees"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "mmf attribute interpretable collaborative filtering"):
  with members:
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "a neural autoregressive approach to collaborative filtering"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "bpmr bayesian probabilistic multivariate ranking"):
  with members:
    - "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.AI(Artificial Intelligence)