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: "edina building an open domain socialbot with self dialogues"
  Abstract: We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition. Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training data for a generative neural network as well as a basis for soft rules used by a matching score component. Each match of a soft rule against a user utterance is associated with a confidence score which we show is strongly indicative of reply quality, allowing this component to self-censor and be effectively integrated with other components. Edina's full architecture features a rule-based system backing off to a matching score, backing off to a generative neural network. Our hybrid data-driven methodology thus addresses both coverage limitations of a strictly rule-based approach and the lack of guarantees of a strictly machine-learning approach.

Connected through hyperedge HE-1 (induced by source paper "crwiz a framework for crowdsourcing real time wizard of oz dialogues"):
  with members:
    - "natural language interaction to facilitate mental models of remote robots"
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "end to end trainable non collaborative dialog system"
    - "the orca hub explainable offshore robotics through intelligent interfaces"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "frames a corpus for adding memory to goal oriented dialogue systems"

Connected through hyperedge HE-2 (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-3 (induced by source paper "taskmaster 1 toward a realistic and diverse dialog dataset"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "towards exploiting background knowledge for building conversation systems"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "wavenet a generative model for raw audio"
    - "learning end to end goal oriented dialog"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-4 (induced by source paper "implicit discourse relation identification for open domain dialogues"):
  with members:
    - "advancing the state of the art in open domain dialog systems through the alexa prize"
    - "deep enhanced representation for implicit discourse relation recognition"
    - "talking to myself self dialogues as data for conversational agents"
    - "deep contextualized word representations"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "conversational ai the science behind the alexa prize"

Connected through hyperedge HE-5 (induced by source paper "slugbot developing a computational model andframework of a novel dialogue genre"):
  with members:
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "attention is all you need"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"
    - "personabank a corpus of personal narratives and their story intention graphs"
    - "m2d monolog to dialog generation for conversational story telling"

Connected through hyperedge HE-6 (induced by source paper "towards universal dialogue act tagging for task oriented dialogues"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "iso standard domain independent dialogue act tagging for conversational agents"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "enriching word vectors with subword information"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "towards deep conversational recommendations"):
  with members:
    - "conversational recommender system"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "pointing the unknown words"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-8 (induced by source paper "neural response ranking for social conversation a data efficient approach"):
  with members:
    - "avoiding echo responses in a retrieval based conversation system"
    - "billion scale similarity search with gpus"
    - "a practical approach to dialogue response generation in closed domains"
    - "rubystar a non task oriented mixture model dialog system"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "an ensemble model with ranking for social dialogue"
    - "deep reinforcement learning for dialogue generation"
    - "conversational contextual cues the case of personalization and history for response ranking"

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)