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: "sentiment adaptive end to end dialog systems"
  Abstract: End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating. However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. We incorporated user sentiment information in both supervised and reinforcement learning settings. In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. This work is the first attempt to incorporate multimodal user information in the adaptive end-to-end dialog system training framework and attained state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "emma an emotionally intelligent personal assistant for improving wellbeing"
    - "finding good representations of emotions for text classification"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "affect lm a neural language model for customizable affective text generation"

Connected through hyperedge HE-2 (induced by source paper "effects of persuasive dialogues testing bot identities and inquiry strategies"):
  with members:
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "how to build user simulators to train rl based dialog systems"
    - "decoupling strategy and generation in negotiation dialogues"
    - "learning to generate reviews and discovering sentiment"
    - "enriching word vectors with subword information"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"

Connected through hyperedge HE-3 (induced by source paper "way off policy batch deep reinforcement learning of implicit human preferences in dialog"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "off policy deep reinforcement learning without exploration"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-4 (induced by source paper "how to build user simulators to train rl based dialog systems"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "unifying human and statistical evaluation for natural language generation"
    - "unsupervised dialog structure learning"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "decoupling strategy and generation in negotiation dialogues"
    - "building a conversational agent overnight with dialogue self play"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "domain adaptive dialog generation via meta learning"):
  with members:
    - "few shot generalization across dialogue tasks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning for low resource neural machine translation"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "prototypical networks for few shot learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"

Connected through hyperedge HE-6 (induced by source paper "guided dialog policy learning reward estimation for multi domain task oriented dialog"):
  with members:
    - "convlab multi domain end to end dialog system platform"
    - "adversarial learning of task oriented neural dialog models"
    - "user modeling for task oriented dialogues"
    - "decoupling strategy and generation in negotiation dialogues"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "fine grained sentence functions for short text conversation"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "controlling linguistic style aspects in neural language generation"
    - "steering output style and topic in neural response generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "stalematebreaker a proactive content introducing approach to automatic human computer conversation"
    - "topic aware neural response generation"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-8 (induced by source paper "unsupervised dialog structure learning"):
  with members:
    - "zero shot dialog generation with cross domain latent actions"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "quantized dialog language model for goal oriented conversational systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a network based end to end trainable task oriented dialogue system"
    - "language as a latent variable discrete generative models for sentence compression"

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)