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: "topic based evaluation for conversational bots"
  Abstract: Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.

Connected through hyperedge HE-1 (induced by source paper "survey on evaluation methods for dialogue systems"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "coqa a conversational question answering challenge"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"
    - "quac question answering in context"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"):
  with members:
    - "learning to write with cooperative discriminators"
    - "an efficient framework for learning sentence representations"
    - "contextual topic modeling for dialog systems"
    - "coherence models for dialogue"
    - "conversational ai the science behind the alexa prize"
    - "on evaluating and comparing open domain dialog systems"
    - "attention is all you need"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"

Connected through hyperedge HE-3 (induced by source paper "what makes a good conversation how controllable attributes affect human judgments"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"
    - "extending neural generative conversational model using external knowledge sources"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "predictive engagement an efficient metric for automatic evaluation of open domain dialogue systems"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "unifying human and statistical evaluation for natural language generation"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (induced by source paper "advancing the state of the art in open domain dialog systems through the alexa prize"):
  with members:
    - "slugbot developing a computational model andframework of a novel dialogue genre"
    - "achieving human parity on automatic chinese to english news translation"
    - "contextual language model adaptation for conversational agents"
    - "alquist the alexa prize socialbot"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
    - "conversational ai the science behind the alexa prize"
    - "just ask building an architecture for extensible self service spoken language understanding"

Connected through hyperedge HE-6 (induced by source paper "abstractive and extractive text summarization using document context vector and recurrent neural networks"):
  with members:
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "conversational ai the science behind the alexa prize"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"
    - "sequence to sequence learning as beam search optimization"
    - "neural summarization by extracting sentences and words"
    - "sequence to sequence rnns for text summarization"

Connected through hyperedge HE-7 (induced by source paper "contextual topic modeling for dialog systems"):
  with members:
    - "conversational ai the science behind the alexa prize"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "a diversity promoting objective function for neural conversation models"
    - "probabilistic latent semantic analysis"

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)