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: "what makes a good conversation how controllable attributes affect human judgments"
  Abstract: A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking. We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task. We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.

Connected through hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"

Connected through hyperedge HE-2 (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-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"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (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"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "team qcri mit at semeval 2019 task 4 propaganda analysis meets hyperpartisan news detection"

Connected through hyperedge HE-6 (induced by source paper "the dialogue dodecathlon open domain knowledge and image grounded conversational agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "lxmert learning cross modality encoder representations from transformers"
    - "the curious case of neural text degeneration"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-7 (induced by source paper "plug and play language models a simple approach to controlled text generation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "the curious case of neural text degeneration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuning language models from human preferences"
    - "putting machine translation in context with the noisy channel model"

Connected through hyperedge HE-8 (induced by source paper "ask to learn a study on curiosity driven question generation"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "question answering as an automatic evaluation metric for news article summarization"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "analyzing uncertainty in neural machine translation"
    - "towards a better metric for evaluating question generation systems"
    - "bottom up abstractive summarization"

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)