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: "neural rating regression with abstractive tips generation for recommendation"
  Abstract: Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate'' user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.

Connected through hyperedge HE-1 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning"):
  with members:
    - "micro and macro level churn analysis of large scale mobile games"
    - "joint neural collaborative filtering for recommender systems"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-2 (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 via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "hybrid deep embedding for recommendations with dynamic aspect level explanations"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "self attentive sequential recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep neural review text interaction for recommendation systems"):
  with members:
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "text matching as image recognition"
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "learning distributed representations from reviews for collaborative filtering"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "persona aware tips generation"):
  with members:
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "actor critic based training framework for abstractive summarization"
    - "quase sequence editing under quantifiable guidance"
    - "toward controlled generation of text"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "automated crowdturfing attacks and defenses in online review systems"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-6 (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"
    - "automatic generation of natural language explanations"

Connected through hyperedge HE-7 (induced by source paper "from personalization to privatization meta matrix factorization for private rating predictions"):
  with members:
    - "neural variational hybrid collaborative filtering"
    - "on first order meta learning algorithms"
    - "outer product based neural collaborative filtering"
    - "a collective variational autoencoder for top n recommendation with side information"
    - "federated meta learning for recommendation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "federated meta learning with fast convergence and efficient communication"
    - "neural collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "seer an explainable deep learning midi based hybrid song recommender system"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "machine learning approaches to hybrid music recommender systems"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "neural collaborative filtering"
    - "a deep multimodal approach for cold start music recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "a critical review of recurrent neural networks for sequence learning"

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.CL(Computation and Language)