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: "an attentional neural conversation model with improved specificity"
  Abstract: In this paper we propose a neural conversation model for conducting dialogues. We demonstrate the use of this model to generate help desk responses, where users are asking questions about PC applications. Our model is distinguished by two characteristics. First, it models intention across turns with a recurrent network, and incorporates an attention model that is conditioned on the representation of intention. Secondly, it avoids generating non-specific responses by incorporating an IDF term in the objective function. The model is evaluated both as a pure generation model in which a help-desk response is generated from scratch, and as a retrieval model with performance measured using recall rates of the correct response. Experimental results indicate that the model outperforms previously proposed neural conversation architectures, and that using specificity in the objective function significantly improves performances for both generation and retrieval.

Connected through hyperedge HE-1 (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-2 (induced by source paper "listening between the lines learning personal attributes from conversations"):
  with members:
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "hierarchical recurrent attention network for response generation"

Connected through hyperedge HE-3 (induced by source paper "recent advances in conversational nlp towards the standardization of chatbot building"):
  with members:
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "adversarial learning for neural dialogue generation"
    - "a deep reinforcement learning chatbot"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "sequence to sequence rnns for text summarization"

Connected through hyperedge HE-4 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "flexible and scalable state tracking framework for goal oriented dialogue systems"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "coherent dialogue with attention based language models"):
  with members:
    - "a persona based neural conversation model"
    - "a decomposable attention model for natural language inference"
    - "a context aware natural language generator for dialogue systems"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "lstm based conversation models"
    - "recurrent memory network for language modeling"
    - "show attend and tell neural image caption generation with visual attention"
    - "a neural network approach to context sensitive generation of conversational responses"

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)