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: "controlling personality based stylistic variation with neural natural language generators"
  Abstract: Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a statistical generator, Personage, to synthesize a new corpus of over 88,000 restaurant domain utterances whose style varies according to models of personality, giving us total control over both the semantic content and the stylistic variation in the training data. We then vary the amount of explicit stylistic supervision given to the three models. We show that our most explicit model can simultaneously achieve high fidelity to both semantic and stylistic goals: this model adds a context vector of 36 stylistic parameters as input to the hidden state of the encoder at each time step, showing the benefits of explicit stylistic supervision, even when the amount of training data is large.

Connected through hyperedge HE-1 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"

Connected through hyperedge HE-2 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "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"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-3 (induced by source paper "queens are powerful too mitigating gender bias in dialogue generation"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "does gender matter towards fairness in dialogue systems"
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "gender bias in contextualized word embeddings"
    - "what makes a good conversation how controllable attributes affect human judgments"

Connected through hyperedge HE-4 (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg"):
  with members:
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "opennmt neural machine translation toolkit"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"

Connected through hyperedge HE-5 (induced by source paper "generating summaries tailored to target characteristics"):
  with members:
    - "multi task neural models for translating between styles within and across languages"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "maximizing stylistic control and semantic accuracy in nlg personality variation and discourse contrast"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "neural multivoice models for expressing novel personalities in dialog"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "learning neural templates for text generation"
    - "toward controlled generation of text"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "tree structured semantic encoder with knowledge sharing for domain adaptation in natural language generation"):
  with members:
    - "variational cross domain natural language generation for spoken dialogue systems"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "the e2e dataset new challenges for end to end generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "improved semantic representations from tree structured long short term memory networks"
    - "multi domain dialog state tracking using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "low level linguistic controls for style transfer and content preservation"):
  with members:
    - "what is wrong with style transfer for texts"
    - "language style transfer from sentences with arbitrary unknown styles"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "controlling linguistic style aspects in neural language generation"
    - "style transfer in text exploration and evaluation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "bag of tricks for efficient text classification"
    - "a neural algorithm of artistic style"

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)