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 from minimal data the generalisation power of dialogue grammars"
  Abstract: We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74% of the Facebook AI bAbI dataset even when trained on only 0.13% of the data (5 dialogues). It can in addition process 65% of bAbI+, a corpus we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-the-art retrieval model, MemN2N. We find that, in terms of semantic accuracy, MemN2N shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.

Connected through hyperedge HE-1 (induced by source paper "incremental learning from scratch for task oriented dialogue systems"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "deep active learning for named entity recognition"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a structured self attentive sentence embedding"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

Connected through hyperedge HE-2 (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 using linguistic knowledge to learn from minimal data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "zero shot dialog generation with cross domain latent actions"):
  with members:
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "key value retrieval networks for task oriented dialogue"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"
    - "one shot imitation learning"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-4 (induced by source paper "analysing the potential of seq to seq models for incremental interpretation in task oriented dialogue"):
  with members:
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"
    - "a neural conversational model"
    - "adam a method for stochastic optimization"
    - "strongly incremental repair detection"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "exploring semantic incrementality with dynamic syntax and vector space semantics"):
  with members:
    - "distributional inclusion hypothesis for tensor based composition"
    - "compositional distributional semantics with compact closed categories and frobenius algebras"
    - "mathematical foundations for a compositional distributional model of meaning"

Connected through hyperedge HE-6 (induced by source paper "toward continual learning for conversational agents"):
  with members:
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "a network based end to end trainable task oriented dialogue system"
    - "less forgetting learning in deep neural networks"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "overcoming catastrophic forgetting in neural networks"

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)