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: "on the evaluation of dialogue systems with next utterance classification"
  Abstract: An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has proposed Next-Utterance-Classification (NUC) as a surrogate task for building dialogue systems from text data. In this paper we investigate the performance of humans on this task to validate the relevance of NUC as a method of evaluation. Our results show three main findings: (1) humans are able to correctly classify responses at a rate much better than chance, thus confirming that the task is feasible, (2) human performance levels vary across task domains (we consider 3 datasets) and expertise levels (novice vs experts), thus showing that a range of performance is possible on this type of task, (3) automated dialogue systems built using state-of-the-art machine learning methods have similar performance to the human novices, but worse than the experts, thus confirming the utility of this class of tasks for driving further research in automated dialogue systems.

Connected through hyperedge HE-1 (induced by source paper "pretraining methods for dialog context representation learning"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-2 (induced by source paper "answer based adversarial training for generating clarification questions"):
  with members:
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "a persona based neural conversation model"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-3 (induced by source paper "triplenet triple attention network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "a structured self attentive sentence embedding"
    - "bidirectional attention flow for machine comprehension"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "syntax based deep matching of short texts"

Connected through hyperedge HE-4 (induced by source paper "towards empathetic open domain conversation models a new benchmark and dataset"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "multi task deep neural networks for natural language understanding"
    - "learning semantic textual similarity from conversations"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "training millions of personalized dialogue agents"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (induced by source paper "the rllchatbot a solution to the convai challenge"):
  with members:
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "a deep reinforcement learning chatbot"
    - "ethical challenges in data driven dialogue systems"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "improving retrieval modeling using cross convolution networks and multi frequency word embedding"):
  with members:
    - "self guiding multimodal lstm when we do not have a perfect training dataset for image captioning"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "syntax based deep matching of short texts"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-7 (induced by source paper "training millions of personalized dialogue agents"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "personalization in goal oriented dialog"
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-8 (induced by source paper "achieving fluency and coherency in task oriented dialog"):
  with members:
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "learning end to end goal oriented dialog"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "a survey of available corpora for building data driven dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural responding machine for short text conversation"

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)