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: "advancing the state of the art in open domain dialog systems through the alexa prize"
  Abstract: Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses. The 2018 competition also included the provision of a suite of tools and models to the competitors including the CoBot (conversational bot) toolkit, topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could focus on building knowledge-rich, coherent and engaging multi-turn dialog systems. This paper outlines the advances developed by the university teams as well as the Alexa Prize team to achieve the common goal of advancing the science of Conversational AI. We address several key open-ended problems such as conversational speech recognition, open domain natural language understanding, commonsense reasoning, statistical dialog management, and dialog evaluation. These collaborative efforts have driven improved experiences by Alexa users to an average rating of 3.61, the median duration of 2 mins 18 seconds, and average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch of the 2018 competition. For conversational speech recognition, we have improved our relative Word Error Rate by 55% and our relative Entity Error Rate by 34% since the launch of the Alexa Prize. Socialbots improved in quality significantly more rapidly in 2018, in part due to the release of the CoBot toolkit.

Connected through hyperedge HE-1 (induced by source paper "proposal towards a personalized knowledge powered self play based ensemble dialog system"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "improving neural conversational models with entropy based data filtering"
    - "deep learning based chatbot models"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "exploration by random network distillation"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "why do neural response generation models prefer universal replies"

Connected through hyperedge HE-2 (induced by source paper "viggo a video game corpus for data to text generation in open domain conversation"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"
    - "building a conversational agent overnight with dialogue self play"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "towards exploiting background knowledge for building conversation systems"
    - "attention is all you need"
    - "learning robust dialog policies in noisy environments"
    - "multi domain neural network language generation for spoken dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "implicit discourse relation identification for open domain dialogues"):
  with members:
    - "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"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"

Connected through hyperedge HE-4 (induced by source paper "entertaining and opinionated but too controlling a large scale user study of an open domain alexa prize system"):
  with members:
    - "slugbot developing a computational model andframework of a novel dialogue genre"
    - "conversational ai the science behind the alexa prize"
    - "personabank a corpus of personal narratives and their story intention graphs"
    - "a deep reinforcement learning chatbot"
    - "a persona based neural conversation model"
    - "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"
    - "a neural conversational model"

Connected through hyperedge HE-5 (induced by source paper "a framework for building closed domain chat dialogue systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "adversarial learning for neural dialogue generation"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural conversational model"

Connected through hyperedge HE-6 (induced by source paper "measuring conversational fluidity in automated dialogue agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "lida lightweight interactive dialogue annotator"):
  with members:
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-8 (induced by source paper "the design and implementation of xiaoice an empathetic social chatbot"):
  with members:
    - "challenges in building intelligent open domain dialog systems"
    - "response generation by context aware prototype editing"
    - "image inspired poetry generation in xiaoice"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "neural approaches to conversational ai"
    - "a study on dialogue reward prediction for open ended conversational agents"
    - "sounding board a user centric and content driven social chatbot"

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)