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: "towards an automatic turing test learning to evaluate dialogue responses"
  Abstract: Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.

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 "avgout a simple output probability measure to eliminate dull responses"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "improving abstraction in text summarization"
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "polite dialogue generation without parallel data"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "learning to decode for future success"
    - "generating high quality and informative conversation responses with sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents"):
  with members:
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-4 (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"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-5 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "neural response ranking for social conversation a data efficient approach"
    - "automatic evaluation of neural personality based chatbots"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"

Connected through hyperedge HE-6 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-7 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

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)