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 optimization of task oriented dialogue model with deep reinforcement learning"
  Abstract: In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL). The system is able to track dialogue state, interface with knowledge bases, and incorporate query results into agent's responses to successfully complete task-oriented dialogues. Dialogue policy learning is conducted with a hybrid supervised and deep RL methods. We first train the dialogue agent in a supervised manner by learning directly from task-oriented dialogue corpora, and further optimize it with deep RL during its interaction with users. In the experiments on two different dialogue task domains, our model demonstrates robust performance in tracking dialogue state and producing reasonable system responses. We show that deep RL based optimization leads to significant improvement on task success rate and reduction in dialogue length comparing to supervised training model. We further show benefits of training task-oriented dialogue model end-to-end comparing to component-wise optimization with experiment results on dialogue simulations and human evaluations.

Connected through hyperedge HE-1 (induced by source paper "learning to prove theorems via interacting with proof assistants"):
  with members:
    - "holist an environment for machine learning of higher order theorem proving"
    - "code2vec learning distributed representations of code"
    - "gamepad a learning environment for theorem proving"
    - "generative code modeling with graphs"
    - "reinforcement learning of theorem proving"
    - "learning to prove with tactics"
    - "end to end differentiable proving"
    - "deep network guided proof search"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-4 (induced by source paper "integrating planning for task completion dialogue policy learning"):
  with members:
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "the predictron end to end learning and planning"

Connected through hyperedge HE-5 (induced by source paper "chat more if you like dynamic cue words planning to flow longer conversations"):
  with members:
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-6 (induced by source paper "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "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"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "a persona based neural conversation model"

Connected through hyperedge HE-7 (induced by source paper "an end to end approach for handling unknown slot values in dialogue state tracking"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "scalable multi domain dialogue state tracking"
    - "get to the point summarization with pointer generator networks"
    - "end to end task completion neural dialogue systems"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "pointing the unknown words"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "subgoal discovery for hierarchical dialogue policy learning"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "feudal networks for hierarchical reinforcement learning"
    - "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"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"

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)