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: "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
  Abstract: Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, and self-corrections. But training data for machine learning approaches to dialogue processing is often either cleaned-up or wholly synthetic in order to avoid such phenomena. The question then arises of how well systems trained on such clean data generalise to real spontaneous dialogue, or indeed whether they are trainable at all on naturally occurring dialogue data. To answer this question, we created a new corpus called bAbI+ by systematically adding natural spontaneous incremental dialogue phenomena such as restarts and self-corrections to the Facebook AI Research's bAbI dialogues dataset. We then explore the performance of a state-of-the-art retrieval model, MemN2N, on this more natural dataset. Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so. Finally, we go on to show that an incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both bAbI and bAbI+, highlighting the generalisation properties of linguistically informed dialogue models.

Connected through hyperedge HE-1 (induced by source paper "hybrid generative retrieval transformers for dialogue domain adaptation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "data efficient goal oriented conversation with dialogue knowledge transfer networks"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "addressing objects and their relations the conversational entity dialogue model"
    - "unsupervised dialog structure learning"
    - "few shot generalization across dialogue tasks"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "latent intention dialogue models"

Connected through hyperedge HE-3 (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"
    - "latent intention dialogue models"
    - "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"
    - "bootstrapping incremental dialogue systems using linguistic knowledge to learn from minimal data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "context aware dialog re ranking for task oriented dialog systems"):
  with members:
    - "state of the art speech recognition with sequence to sequence 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"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "the microsoft 2016 conversational speech recognition system"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "end to end memory networks"
    - "improved deep learning baselines for ubuntu corpus dialogs"

Connected through hyperedge HE-5 (induced by source paper "analysing the potential of seq to seq models for incremental interpretation in task oriented dialogue"):
  with members:
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"
    - "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-6 (induced by source paper "improving robustness of neural dialog systems in a data efficient way with turn dropout"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "toward continual learning for conversational agents"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "neural sentence embedding using only in domain sentences for out of domain sentence detection in dialog systems"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "learning end to end goal oriented dialog"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-7 (induced by source paper "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"):
  with members:
    - "multi domain neural network language generation for spoken dialogue systems"
    - "bootstrapping incremental dialogue systems using linguistic knowledge to learn from minimal data"

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)