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: "data distillation for controlling specificity in dialogue generation"
  Abstract: People speak at different levels of specificity in different situations. Depending on their knowledge, interlocutors, mood, etc.} A conversational agent should have this ability and know when to be specific and when to be general. We propose an approach that gives a neural network--based conversational agent this ability. Our approach involves alternating between \emph{data distillation} and model training : removing training examples that are closest to the responses most commonly produced by the model trained from the last round and then retrain the model on the remaining dataset. Dialogue generation models trained with different degrees of data distillation manifest different levels of specificity. #R##N#We then train a reinforcement learning system for selecting among this pool of generation models, to choose the best level of specificity for a given input. Compared to the original generative model trained without distillation, the proposed system is capable of generating more interesting and higher-quality responses, in addition to appropriately adjusting specificity depending on the context. #R##N#Our research constitutes a specific case of a broader approach involving training multiple subsystems from a single dataset distinguished by differences in a specific property one wishes to model. We show that from such a set of subsystems, one can use reinforcement learning to build a system that tailors its output to different input contexts at test time.

Connected through hyperedge HE-1 (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-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"
    - "topic based evaluation for conversational bots"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards an engine for lifelong interactive knowledge learning in human machine conversations"):
  with members:
    - "listen interact and talk learning to speak via interaction"
    - "active one shot learning"
    - "naturalizing a programming language via interactive learning"
    - "a knowledge grounded neural conversation model"
    - "adversarial learning for neural dialogue generation"
    - "open world knowledge graph completion"
    - "learning language games through interaction"
    - "topic aware neural response generation"

Connected through hyperedge HE-4 (induced by source paper "generating responses expressing emotion in an open domain dialogue system"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "fasttext zip compressing text classification models"
    - "a structured self attentive sentence embedding"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

Connected through hyperedge HE-5 (induced by source paper "automatic evaluation of neural personality based chatbots"):
  with members:
    - "controlling linguistic style aspects in neural language generation"
    - "multi task learning for speaker role adaptation in neural conversation models"
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "generative deep neural networks for dialogue a short review"
    - "attention with intention for a neural network conversation model"
    - "a diversity promoting objective function for neural conversation models"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "self attention based message relevant response generation for neural conversation model"):
  with members:
    - "attention is all you need"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "a diversity promoting objective function for neural conversation models"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"

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)