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: "learning distributed representations from reviews for collaborative filtering"
  Abstract: Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Connected through hyperedge HE-1 (induced by source paper "deep neural review text interaction for recommendation systems"):
  with members:
    - "neural rating regression with abstractive tips generation for recommendation"
    - "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"
    - "collaborative deep learning for recommender systems"

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"
    - "automatic generation of natural language explanations"

Connected through hyperedge HE-3 (induced by source paper "constructing high precision knowledge bases with subjective and factual attributes"):
  with members:
    - "subjective knowledge acquisition and enrichment powered by crowdsourcing"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "wide deep learning for recommender systems"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "collaborative deep learning for recommender systems"
    - "scalable recommendation with poisson factorization"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "sequence aware recommender systems"):
  with members:
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "personalized course sequence recommendations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-5 (induced by source paper "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"):
  with members:
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "node2vec scalable feature learning for networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "evaluating neural word representations in tensor based compositional settings"

Connected through hyperedge HE-6 (induced by source paper "regularizing matrix factorization with user and item embeddings for recommendation"):
  with members:
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "item2vec neural item embedding for collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "hierarchical neural language models for joint representation of streaming documents and their content"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "improving explainable recommendations with synthetic reviews"):
  with members:
    - "automatic generation of natural language explanations"
    - "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"
    - "visualizing and understanding recurrent networks"
    - "capturing meaning in product reviews with character level generative text models"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural rating regression with abstractive tips generation for recommendation"):
  with members:
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "abstractive multi document summarization via phrase selection and merging"
    - "collaborative deep learning for recommender systems"

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.LG(Machine Learning)