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: "the price of debiasing automatic metrics in natural language evaluation"
  Abstract: For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias against certain model improvements. On the other hand, averaging human judgments, the unbiased gold standard, is often too expensive. In this paper, we use control variates to combine automatic metrics with human evaluation to obtain an unbiased estimator with lower cost than human evaluation alone. In practice, however, we obtain only a 7-13% cost reduction on evaluating summarization and open-response question answering systems. We then prove that our estimator is optimal: there is no unbiased estimator with lower cost. Our theory further highlights the two fundamental bottlenecks---the automatic metric and the prompt shown to human evaluators---both of which need to be improved to obtain greater cost savings.

Connected through hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "the evolved transformer"

Connected through hyperedge HE-2 (induced by source paper "learning to compare for better training and evaluation of open domain natural language generation models"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "bertscore evaluating text generation with bert"
    - "judge the judges a large scale evaluation study of neural language models for online review generation"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-4 (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"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "language gans falling short"
    - "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-5 (induced by source paper "moverscore text generation evaluating with contextualized embeddings and earth mover distance"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "bertscore evaluating text generation with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "linguistic knowledge and transferability of contextual representations"
    - "better rewards yield better summaries learning to summarise without references"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a simple theoretical model of importance for summarization"

Connected through hyperedge HE-6 (induced by source paper "automatically neutralizing subjective bias in text"):
  with members:
    - "towards controlled transformation of sentiment in sentences"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "evaluating style transfer for text"
    - "identifying and reducing gender bias in word level language models"
    - "understanding undesirable word embedding associations"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "delete retrieve generate a simple approach to sentiment and style transfer"

Connected through hyperedge HE-7 (induced by source paper "optimizing the factual correctness of a summary a study of summarizing radiology reports"):
  with members:
    - "evaluating the factual consistency of abstractive text summarization"
    - "assessing the factual accuracy of generated text"
    - "ontology aware clinical abstractive summarization"
    - "generating wikipedia by summarizing long sequences"
    - "on the automatic generation of medical imaging reports"
    - "banditsum extractive summarization as a contextual bandit"
    - "learning to summarize radiology findings"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-8 (induced by source paper "communication based evaluation for natural language generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "pragmatically informative text generation"
    - "generating bilingual pragmatic color references"
    - "translating neuralese"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "learning to summarize radiology findings"
    - "sentence level fluency evaluation references help but can be spared"
    - "context aware captions from context agnostic supervision"

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)