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: "bootstrapping incremental dialogue systems using linguistic knowledge to learn from minimal data"
  Abstract: We present a method for inducing new dialogue systems from very small amounts of unannotated dialogue data, showing how word-level exploration using Reinforcement Learning (RL), combined with an incremental and semantic grammar - Dynamic Syntax (DS) - allows systems to discover, generate, and understand many new dialogue variants. The method avoids the use of expensive and time-consuming dialogue act annotations, and supports more natural (incremental) dialogues than turn-based systems. Here, language generation and dialogue management are treated as a joint decision/optimisation problem, and the MDP model for RL is constructed automatically. With an implemented system, we show that this method enables a wide range of dialogue variations to be automatically captured, even when the system is trained from only a single dialogue. The variants include question-answer pairs, over- and under-answering, self- and other-corrections, clarification interaction, split-utterances, and ellipsis. This generalisation property results from the structural knowledge and constraints present within the DS grammar, and highlights some limitations of recent systems built using machine learning techniques only.

Connected through hyperedge HE-1 (induced by source paper "few shot dialogue generation without annotated data a transfer learning approach"):
  with members:
    - "addressing objects and their relations the conversational entity dialogue model"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "latent intention dialogue models"
    - "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"):
  with members:
    - "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"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"

Connected through hyperedge HE-3 (induced by source paper "learning how to learn an adaptive dialogue agent for incrementally learning visually grounded word meanings"):
  with members:
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "training an adaptive dialogue policy for interactive learning of visually grounded word meanings"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-4 (induced by source paper "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"):
  with members:
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "multi domain neural network language generation for spoken dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "the burchak corpus a challenge data set for interactive learning of visually grounded word meanings"):
  with members:
    - "a sequence to sequence model for user simulation in spoken dialogue systems"
    - "training an adaptive dialogue policy for interactive learning of visually grounded word meanings"

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)