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: "quase sequence editing under quantifiable guidance"
  Abstract: We propose the task of Quantifiable Sequence Editing (QuaSE): editing an input sequence to generate an output sequence that satisfies a given numerical outcome value measuring a certain property of the sequence, with the requirement of keeping the main content of the input sequence. For example, an input sequence could be a word sequence, such as review sentence and advertisement text. For a review sentence, the outcome could be the review rating; for an advertisement, the outcome could be the click-through rate. The major challenge in performing QuaSE is how to perceive the outcome-related wordings, and only edit them to change the outcome. In this paper, the proposed framework contains two latent factors, namely, outcome factor and content factor, disentangled from the input sentence to allow convenient editing to change the outcome and keep the content. Our framework explores the pseudo-parallel sentences by modeling their content similarity and outcome differences to enable a better disentanglement of the latent factors, which allows generating an output to better satisfy the desired outcome and keep the content. The dual reconstruction structure further enhances the capability of generating expected output by exploiting the couplings of latent factors of pseudo-parallel sentences. For evaluation, we prepared a dataset of Yelp review sentences with the ratings as outcome. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.

Connected through hyperedge HE-1 (induced by source paper "rethinking text attribute transfer a lexical analysis"):
  with members:
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "a dual reinforcement learning framework for unsupervised text style transfer"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"

Connected through hyperedge HE-2 (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"
    - "toward controlled generation of text"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "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-3 (induced by source paper "semi supervised text style transfer cross projection in latent space"):
  with members:
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "unsupervised stylish image description generation via domain layer norm"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "difficulty controllable generation of reading comprehension questions"
    - "language style transfer from sentences with arbitrary unknown styles"

Connected through hyperedge HE-4 (induced by source paper "decomposable neural paraphrase generation"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "a task in a suit and a tie paraphrase generation with semantic augmentation"
    - "learning neural templates for text generation"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "toward controlled generation of text"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "difficulty controllable generation of reading comprehension questions"):
  with members:
    - "generating distractors for reading comprehension questions from real examinations"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "title guided encoding for keyphrase generation"
    - "learning to ask neural question generation for reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "neural question generation from text a preliminary study"

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)