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: "end to end conversation modeling track in dstc6"
  Abstract: End-to-end training of neural networks is a promising approach to automatic construction of dialog systems using a human-to-human dialog corpus. Recently, Vinyals et al. tested neural conversation models using OpenSubtitles. Lowe et al. released the Ubuntu Dialogue Corpus for researching unstructured multi-turn dialogue systems. Furthermore, the approach has been extended to accomplish task oriented dialogs to provide information properly with natural conversation. For example, Ghazvininejad et al. proposed a knowledge grounded neural conversation model [3], where the research is aiming at combining conversational dialogs with task-oriented knowledge using unstructured data such as Twitter data for conversation and Foursquare data for external knowledge.However, the task is still limited to a restaurant information service, and has not yet been tested with a wide variety of dialog tasks. In addition, it is still unclear how to create intelligent dialog systems that can respond like a human agent. #R##N#In consideration of these problems, we proposed a challenge track to the 6th dialog system technology challenges (DSTC6) using human-to-human dialog data to mimic human dialog behaviors. The focus of the challenge track is to train end-to-end conversation models from human-to-human conversation and accomplish end-to-end dialog tasks in various situations assuming a customer service, in which a system plays a role of human agent and generates natural and informative sentences in response to user's questions or comments given dialog context.

Connected through hyperedge HE-1 (induced by source paper "knowledge grounded response generation with deep attentional latent variable model"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "understanding convolutional neural networks for text classification"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "neural approaches to conversational ai"
    - "variational memory encoder decoder"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "re evaluating adem a deeper look at scoring dialogue responses"):
  with members:
    - "towards a better metric for evaluating question generation systems"
    - "deep text classification can be fooled"
    - "neural approaches to conversational ai"
    - "towards building large scale multimodal domain aware conversation systems"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generating natural adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "time to take emoji seriously they vastly improve casual conversational models"):
  with members:
    - "a repository of conversational datasets"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "training millions of personalized dialogue agents"
    - "a large scale corpus for conversation disentanglement"
    - "coqa a conversational question answering challenge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "self representation on twitter using emoji skin color modifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "dialog system technology challenge 7"):
  with members:
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "attention strategies for multi source sequence to sequence learning"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "enhanced lstm for natural language inference"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "analyzing and characterizing user intent in information seeking conversations"):
  with members:
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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)