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: "end to end lstm based dialog control optimized with supervised and reinforcement learning"
  Abstract: This paper presents a model for end-to-end learning of task-oriented dialog systems. The main component of the model is a recurrent neural network (an LSTM), which maps from raw dialog history directly to a distribution over system actions. The LSTM automatically infers a representation of dialog history, which relieves the system developer of much of the manual feature engineering of dialog state. In addition, the developer can provide software that expresses business rules and provides access to programmatic APIs, enabling the LSTM to take actions in the real world on behalf of the user. The LSTM can be optimized using supervised learning (SL), where a domain expert provides example dialogs which the LSTM should imitate; or using reinforcement learning (RL), where the system improves by interacting directly with end users. Experiments show that SL and RL are complementary: SL alone can derive a reasonable initial policy from a small number of training dialogs; and starting RL optimization with a policy trained with SL substantially accelerates the learning rate of RL.

Connected through hyperedge HE-1 (induced by source paper "dialogact2vec towards end to end dialogue agent by multi task representation learning"):
  with members:
    - "multi task learning for continuous control"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "global locally self attentive dialogue state tracker"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "learning personalized end to end goal oriented dialog"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "self attentional models application in task oriented dialogue generation systems"):
  with members:
    - "universal transformers"
    - "tensor2tensor for neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "building a conversational agent overnight with dialogue self play"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conversational ai the science behind the alexa prize"
    - "key value retrieval networks for task oriented dialogue"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"):
  with members:
    - "towards universal dialogue state tracking"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "hierarchical text generation and planning for strategic dialogue"

Connected through hyperedge HE-4 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability"):
  with members:
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "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"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-5 (induced by source paper "incorporating joint embeddings into goal oriented dialogues with multi task learning"):
  with members:
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"

Connected through hyperedge HE-6 (induced by source paper "learning end to end goal oriented dialog with maximal user task success and minimal human agent use"):
  with members:
    - "learning end to end goal oriented dialog with multiple answers"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "key value retrieval networks for task oriented dialogue"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "modeling multi action policy for task oriented dialogues"):
  with members:
    - "user modeling for task oriented dialogues"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "recurrently controlled recurrent networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "conversational implicatures in english dialogue annotated dataset"):
  with members:
    - "a repository of conversational datasets"
    - "coqa a conversational question answering challenge"
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "squinky a corpus of sentence level formality informativeness and implicature"

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)