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: "neural text generation with unlikelihood training"
  Abstract: Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top-$k$ and nucleus sampling, they do not address the fact that the token-level probabilities predicted by the model are poor. In this paper we show that the likelihood objective itself is at fault, resulting in a model that assigns too much probability to sequences containing repeats and frequent words, unlike those from the human training distribution. We propose a new objective, unlikelihood training, which forces unlikely generations to be assigned lower probability by the model. We show that both token and sequence level unlikelihood training give less repetitive, less dull text while maintaining perplexity, giving superior generations using standard greedy or beam search. According to human evaluations, our approach with standard beam search also outperforms the currently popular decoding methods of nucleus sampling or beam blocking, thus providing a strong alternative to existing techniques.

Connected through hyperedge HE-1 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "on nmt search errors and model errors cat got your tongue"
    - "hierarchical neural story generation"
    - "correcting length bias in neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-2 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "billsum a corpus for automatic summarization of us legislation"
    - "searching for effective neural extractive summarization what works and what s next"
    - "spanbert improving pre training by representing and predicting spans"

Connected through hyperedge HE-3 (induced by source paper "help anna visual navigation with natural multimodal assistance via retrospective curiosity encouraging imitation learning"):
  with members:
    - "vision and dialog navigation"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "from video game to real robot the transfer between action spaces"
    - "learning to speak and act in a fantasy text adventure game"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-4 (induced by source paper "how decoding strategies affect the verifiability of generated text"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "mixture content selection for diverse sequence generation"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "the curious case of neural text degeneration"
    - "tmlab generative enhanced model gem for adversarial attacks"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-5 (induced by source paper "the curious case of neural text degeneration"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on nmt search errors and model errors cat got your tongue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "on accurate evaluation of gans for language generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-6 (induced by source paper "multi stage pretraining for abstractive summarization"):
  with members:
    - "the curious case of neural text degeneration"
    - "pretraining based natural language generation for text summarization"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "rethinking imagenet pre training"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "entity commonsense representation for neural abstractive summarization"
    - "actor critic based training framework for abstractive summarization"

Connected through hyperedge HE-7 (induced by source paper "don t say that making inconsistent dialogue unlikely with unlikelihood training"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "hellaswag can a machine really finish your sentence"
    - "language models as knowledge bases"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "the curious case of neural text degeneration"
    - "latent relation language models"
    - "counterfactual story reasoning and generation"
    - "negative training for neural dialogue response generation"

Connected through hyperedge HE-8 (induced by source paper "conversation generation with concept flow"):
  with members:
    - "the curious case of neural text degeneration"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "target guided open domain conversation"
    - "deep contextualized word representations"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "a dataset for document grounded conversations"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "sounding board a user centric and content driven social chatbot"

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.LG(Machine Learning)