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: "cgmh constrained sentence generation by metropolis hastings sampling"
  Abstract: In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing language generation techniques are usually based on recurrent neural networks (RNNs). However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last. In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation. CGMH allows complicated constraints such as the occurrence of multiple keywords in the target sentences, which cannot be handled in traditional RNN-based approaches. Moreover, CGMH works in the inference stage, and does not require parallel corpora for training. We evaluate our method on a variety of tasks, including keywords-to-sentence generation, unsupervised sentence paraphrasing, and unsupervised sentence error correction. CGMH achieves high performance compared with previous supervised methods for sentence generation. Our code is released at this https URL

Connected through hyperedge HE-1 (induced by source paper "commongen a constrained text generation dataset towards generative commonsense reasoning"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "codah an adversarially authored question answer dataset for common sense"
    - "bertscore evaluating text generation with bert"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "mirrorgan learning text to image generation by redescription"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "levenshtein transformer"

Connected through hyperedge HE-2 (induced by source paper "araml a stable adversarial training framework for text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "on accurate evaluation of gans for language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"

Connected through hyperedge HE-3 (induced by source paper "generating sentences from disentangled syntactic and semantic spaces"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "bag of words as target for neural machine translation"
    - "syntax directed variational autoencoder for structured data"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "unsupervised paraphrasing by simulated annealing"):
  with members:
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "decomposable neural paraphrase generation"
    - "unsupervised paraphrasing without translation"
    - "building a conversational agent overnight with dialogue self play"
    - "a task in a suit and a tie paraphrase generation with semantic augmentation"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "generating sentences by editing prototypes"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-5 (induced by source paper "pnat non autoregressive transformer by position learning"):
  with members:
    - "imitation learning for non autoregressive neural machine translation"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "attention is all you need"
    - "a conditional variational framework for dialog generation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "towards unsupervised grammatical error correction using statistical machine translation with synthetic comparable corpus"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "unsupervised statistical machine translation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised machine translation"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"

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)