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: "automatic generation of natural language explanations"
  Abstract: An important task for recommender system is to generate explanations according to a user's preferences. Most of the current methods for explainable recommendations use structured sentences to provide descriptions along with the recommendations they produce. However, those methods have neglected the review-oriented way of writing a text, even though it is known that these reviews have a strong influence over user's decision. #R##N#In this paper, we propose a method for the automatic generation of natural language explanations, for predicting how a user would write about an item, based on user ratings from different items' features. We design a character-level recurrent neural network (RNN) model, which generates an item's review explanations using long-short term memories (LSTM). The model generates text reviews given a combination of the review and ratings score that express opinions about different factors or aspects of an item. Our network is trained on a sub-sample from the large real-world dataset BeerAdvocate. Our empirical evaluation using natural language processing metrics shows the generated text's quality is close to a real user written review, identifying negation, misspellings, and domain specific vocabulary.

Connected through hyperedge HE-1 (induced by source paper "an explainable autoencoder for collaborative filtering recommendation"):
  with members:
    - "visually explainable recommendation"
    - "hybrid collaborative recommendation via semi autoencoder"
    - "auto encoding user ratings via knowledge graphs in recommendation scenarios"
    - "capturing meaning in product reviews with character level generative text models"

Connected through hyperedge HE-2 (induced by source paper "ai powered text generation for harmonious human machine interaction current state and future directions"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "learning personalized end to end goal oriented dialog"
    - "multi reward reinforced summarization with saliency and entailment"
    - "learning to generate reviews and discovering sentiment"
    - "key value retrieval networks for task oriented dialogue"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "improving context aware language models"

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 "natural language generation for non expert users"):
  with members:
    - "declarative question answering over knowledge bases containing natural language text with answer set programming"
    - "generating natural language explanations for visual question answering using scene graphs and visual attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "table to text generation by structure aware seq2seq learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "improving explainable recommendations with synthetic reviews"):
  with members:
    - "joint deep modeling of users and items using reviews for recommendation"
    - "medical text classification using convolutional neural networks"
    - "context aware natural language generation with recurrent neural networks"
    - "image based recommendations on styles and substitutes"
    - "learning distributed representations from reviews for collaborative filtering"
    - "visualizing and understanding recurrent networks"
    - "capturing meaning in product reviews with character level generative text models"
    - "on the difficulty of training recurrent neural networks"

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)