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: "evaluating prerequisite qualities for learning end to end dialog systems"
  Abstract: A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals & Le, 2015; Shang et al., 2015). However, this approach leaves many questions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks (Weston et al., 2015b) which are synthetic data that measure the ability of learning machines at various reasoning tasks over toy language. Unfortunately, those tests are very small and hence may encourage methods that do not scale. In this work, we propose a suite of new tasks of a much larger scale that attempt to bridge the gap between the two regimes. Choosing the domain of movies, we provide tasks that test the ability of models to answer factual questions (utilizing OMDB), provide personalization (utilizing MovieLens), carry short conversations about the two, and finally to perform on natural dialogs from Reddit. We provide a dataset covering 75k movie entities and with 3.5M training examples. We present results of various models on these tasks, and evaluate their performance.

Connected through hyperedge HE-1 (induced by source paper "self attentional models application in task oriented dialogue generation systems"):
  with members:
    - "universal transformers"
    - "tensor2tensor for neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "building a conversational agent overnight with dialogue self play"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conversational ai the science behind the alexa prize"
    - "key value retrieval networks for task oriented dialogue"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "towards knowledge based recommender dialog system"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "deep conversational recommender in travel"
    - "graph convolutional neural networks for web scale recommender systems"
    - "towards deep conversational recommendations"
    - "generating wikipedia by summarizing long sequences"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-3 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability"):
  with members:
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-4 (induced by source paper "using a kg copy network for non goal oriented dialogues"):
  with members:
    - "incorporating joint embeddings into goal oriented dialogues with multi task learning"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "improving retrieval modeling using cross convolution networks and multi frequency word embedding"
    - "a trustworthy responsible and interpretable system to handle chit chat in conversational bots"
    - "a knowledge grounded neural conversation model"
    - "chat detection in an intelligent assistant combining task oriented and non task oriented spoken dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "the jddc corpus a large scale multi turn chinese dialogue dataset for e commerce customer service"):
  with members:
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "proactive human machine conversation with explicit conversation goals"
    - "modeling semantic relationship in multi turn conversations with hierarchical latent variables"
    - "building a conversational agent overnight with dialogue self play"
    - "a manually annotated chinese corpus for non task oriented dialogue systems"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"

Connected through hyperedge HE-6 (induced by source paper "incremental learning from scratch for task oriented dialogue systems"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "deep active learning for named entity recognition"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"
    - "a structured self attentive sentence embedding"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-7 (induced by source paper "contextual joint factor acoustic embeddings"):
  with members:
    - "unspeech unsupervised speech context embeddings"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning latent representations for speech generation and transformation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "learning word embeddings from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-8 (induced by source paper "modeling intent dialog policies and response adaptation for goal oriented interactions"):
  with members:
    - "few shot generalization across dialogue tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "starspace embed all the things"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "attention is all you need"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "rasa open source language understanding and dialogue management"

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)