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: "unifying human and statistical evaluation for natural language generation"
  Abstract: How can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity but not quality, as models that occasionally emit low quality samples would be insufficiently penalized. In this paper, we propose a unified framework which evaluates both diversity and quality, based on the optimal error rate of predicting whether a sentence is human- or machine-generated. We demonstrate that this error rate can be efficiently estimated by combining human and statistical evaluation, using an evaluation metric which we call HUSE. On summarization and chit-chat dialogue, we show that (i) HUSE detects diversity defects which fool pure human evaluation and that (ii) techniques such as annealing for improving quality actually decrease HUSE due to decreased diversity.

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"
    - "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"
    - "what makes a good conversation how controllable attributes affect human judgments"

Connected through hyperedge HE-2 (induced by source paper "limits of detecting text generated by large scale language models"):
  with members:
    - "the curious case of neural text degeneration"
    - "human and automatic detection of generated text"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "automated speech generation from un general assembly statements mapping risks in ai generated texts"

Connected through hyperedge HE-3 (induced by source paper "learning to compare for better training and evaluation of open domain natural language generation models"):
  with members:
    - "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"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "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-4 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-5 (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"
    - "scibert a pretrained language model for scientific text"
    - "paws paraphrase adversaries from word scrambling"

Connected through hyperedge HE-6 (induced by source paper "investigating evaluation of open domain dialogue systems with human generated multiple references"):
  with members:
    - "re evaluating adem a deeper look at scoring dialogue responses"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "survey on evaluation methods for dialogue systems"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "importance of a search strategy in neural dialogue modelling"
    - "texygen a benchmarking platform for text generation models"
    - "generating informative and diverse conversational responses via adversarial information maximization"

Connected through hyperedge HE-7 (induced by source paper "the curious case of neural text degeneration"):
  with members:
    - "neural text generation with unlikelihood training"
    - "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-8 (induced by source paper "comparison of diverse decoding methods from conditional language models"):
  with members:
    - "the curious case of neural text degeneration"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "hierarchical neural story generation"
    - "importance of a search strategy in neural dialogue modelling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "opennmt open source toolkit for neural machine 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)