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: "on line active reward learning for policy optimisation in spoken dialogue systems"
  Abstract: The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect. This problem can be mitigated if the user's intent is known in advance or data is available to pre-train a task success predictor off-line. In practice neither of these apply for most real world applications. Here we propose an on-line learning framework whereby the dialogue policy is jointly trained alongside the reward model via active learning with a Gaussian process model. This Gaussian process operates on a continuous space dialogue representation generated in an unsupervised fashion using a recurrent neural network encoder-decoder. The experimental results demonstrate that the proposed framework is able to significantly reduce data annotation costs and mitigate noisy user feedback in dialogue policy learning.

Connected through hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "domain adaptive dialog generation via meta learning"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "unsupervised dialog structure learning"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"

Connected through hyperedge HE-2 (induced by source paper "improving interaction quality estimation with bilstms and the impact on dialogue policy learning"):
  with members:
    - "attention is all you need"
    - "learning from real users rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems"
    - "an application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email"

Connected through hyperedge HE-3 (induced by source paper "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"):
  with members:
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "editing based sql query generation for cross domain context dependent questions"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"

Connected through hyperedge HE-4 (induced by source paper "goal embedded dual hierarchical model for task oriented dialogue generation"):
  with members:
    - "a repository of conversational datasets"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "simple fast accurate intent classification and slot labeling"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "global locally self attentive dialogue state tracker"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-5 (induced by source paper "e3 entailment driven extracting and editing for conversational machine reading"):
  with members:
    - "multi hop knowledge graph reasoning with reward shaping"
    - "interpretation of natural language rules in conversational machine reading"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "global locally self attentive dialogue state tracker"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "game based video context dialogue"):
  with members:
    - "chalet cornell house agent learning environment"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "video highlight prediction using audience chat reactions"
    - "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
    - "a structured self attentive sentence embedding"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "a persona based neural conversation model"

Connected through hyperedge HE-7 (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"
    - "end to end optimization of task oriented dialogue model with deep 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"

Connected through hyperedge HE-8 (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"
    - "end to end optimization of task oriented dialogue model with deep 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)