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: "capturing meaning in product reviews with character level generative text models"
  Abstract: We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by backpropagation through time. Our main results center on a large corpus of 1.5 million beer reviews from BeerAdvocate. In generative mode, our network produces reviews on command, tailored to a star rating or item category. The generative model can also run in reverse, performing classification with surprising accuracy. Performance of the reverse model provides a straightforward way to determine what the generative model knows without relying too heavily on subjective analysis. Given a review, the model can accurately determine the corresponding rating and infer the beer's category (IPA, Stout, etc.). We exploit this capability, tracking perceived sentiment and class membership as each character in a review is processed. Quantitative and qualitative empirical evaluations demonstrate that the model captures meaning and learns nonlinear dynamics in text, such as the effect of negation on sentiment, despite possessing no a priori notion of words. Because the model operates at the character level, it handles misspellings, slang, and large vocabularies without any machinery explicitly dedicated to the purpose.

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"
    - "automatic generation of natural language explanations"
    - "auto encoding user ratings via knowledge graphs in recommendation scenarios"

Connected through hyperedge HE-2 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-3 (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-4 (induced by source paper "controllable unsupervised text attribute transfer via editing entangled latent representation"):
  with members:
    - "language models with transformers"
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "sentiment transfer using seq2seq adversarial autoencoders"

Connected through hyperedge HE-5 (induced by source paper "towards knowledge based personalized product description generation in e commerce"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "extreme adaptation for personalized neural machine translation"
    - "toward controlled generation of text"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"
    - "controlling linguistic style aspects in neural language generation"
    - "adversarial learning for neural dialogue generation"
    - "affective neural response generation"

Connected through hyperedge HE-6 (induced by source paper "towards controllable and personalized review generation"):
  with members:
    - "self attention with relative position representations"
    - "texygen a benchmarking platform for text generation models"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "adversarial ranking for language generation"
    - "improved training of wasserstein gans"
    - "long text generation via adversarial training with leaked information"
    - "automated crowdturfing attacks and defenses in online review systems"

Connected through hyperedge HE-7 (induced by source paper "male bachelor and female ph d have different connotations parallelly annotated stylistic language dataset with multiple personas"):
  with members:
    - "style transfer through back translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "style transfer in text exploration and evaluation"

Connected through hyperedge HE-8 (induced by source paper "explainable fashion recommendation with joint outfit matching and comment generation"):
  with members:
    - "explainable recommendation via multi task learning in opinionated text data"
    - "measuring semantic coherence of a conversation"
    - "learning to generate reviews and discovering sentiment"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "neural collaborative filtering"

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)