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: "content preserving text generation with attribute controls"
  Abstract: In this work, we address the problem of modifying textual attributes of sentences. Given an input sentence and a set of attribute labels, we attempt to generate sentences that are compatible with the conditioning information. To ensure that the model generates content compatible sentences, we introduce a reconstruction loss which interpolates between auto-encoding and back-translation loss components. We propose an adversarial loss to enforce generated samples to be attribute compatible and realistic. Through quantitative, qualitative and human evaluations we demonstrate that our model is capable of generating fluent sentences that better reflect the conditioning information compared to prior methods. We further demonstrate that the model is capable of simultaneously controlling multiple attributes.

Connected through hyperedge HE-1 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-2 (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"
    - "sentiment transfer using seq2seq adversarial autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "my way of telling a story persona based grounded story generation"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "a pipeline for creative visual storytelling"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "style transfer through back translation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"

Connected through hyperedge HE-4 (induced by source paper "domain adaptive text style transfer"):
  with members:
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "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-5 (induced by source paper "toward unsupervised text content manipulation"):
  with members:
    - "text infilling"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "structured content preservation for unsupervised text style transfer"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "revision in continuous space unsupervised text style transfer without adversarial learning"):
  with members:
    - "mu forcing training variational recurrent autoencoders for text generation"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "neural architecture optimization"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-7 (induced by source paper "educating text autoencoders latent representation guidance via denoising"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "universal transformers"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "multiple attribute text style transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-8 (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"

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)