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: "a context aware natural language generator for dialogue systems"
  Abstract: We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses. The generator is based on recurrent neural networks and the sequence-to-sequence approach. It is fully trainable from data which include preceding context along with responses to be generated. We show that the context-aware generator yields significant improvements over the baseline in both automatic metrics and a human pairwise preference test.

Connected through hyperedge HE-1 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-3 (induced by source paper "show price and negotiate a hierarchical attention recurrent visual negotiator"):
  with members:
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "talk the walk navigating new york city through grounded dialogue"
    - "decoupling strategy and generation in negotiation dialogues"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "a survey on dialogue systems recent advances and new frontiers"

Connected through hyperedge HE-4 (induced by source paper "a modular task oriented dialogue system using a neural mixture of experts"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "multi source domain adaptation with mixture of experts"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "understanding back translation at scale"
    - "key value retrieval networks for task oriented dialogue"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "a survey on dialogue systems recent advances and new frontiers"

Connected through hyperedge HE-5 (induced by source paper "modeling intent dialog policies and response adaptation for goal oriented interactions"):
  with members:
    - "few shot generalization across dialogue tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "starspace embed all the things"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "attention is all you need"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "rasa open source language understanding and dialogue management"

Connected through hyperedge HE-6 (induced by source paper "neural multivoice models for expressing novel personalities in dialog"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"
    - "controllable abstractive summarization"
    - "the e2e dataset new challenges for end to end generation"
    - "a persona based neural conversation model"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "crowd sourcing nlg data pictures elicit better data"

Connected through hyperedge HE-7 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

Connected through hyperedge HE-8 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

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)