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: "fully statistical neural belief tracking"
  Abstract: This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models.

Connected through hyperedge HE-1 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"

Connected through hyperedge HE-2 (induced by source paper "a repository of conversational datasets"):
  with members:
    - "designing for health chatbots"
    - "training neural response selection for task oriented dialogue systems"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "domain transfer in dialogue systems without turn level supervision"):
  with members:
    - "towards universal dialogue state tracking"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "scalable multi domain dialogue state tracking"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"

Connected through hyperedge HE-4 (induced by source paper "a simple but effective bert model for dialog state tracking on resource limited systems"):
  with members:
    - "bert dst scalable end to end dialogue state tracking with bidirectional encoder representations from transformer"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "towards universal dialogue state tracking"
    - "deep contextualized word representations"
    - "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"
    - "toward scalable neural dialogue state tracking model"

Connected through hyperedge HE-5 (induced by source paper "sumbt slot utterance matching for universal and scalable belief tracking"):
  with members:
    - "efficient large scale domain classification with personalized attention"
    - "towards universal dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"

Connected through hyperedge HE-6 (induced by source paper "sim a slot independent neural model for dialogue state tracking"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "global locally self attentive dialogue state tracker"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "scalable multi domain dialogue state tracking"
    - "a network based end to end trainable task oriented dialogue system"
    - "neural belief tracker data driven dialogue state tracking"
    - "incremental lstm based dialog state tracker"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "unsupervised post processing of word vectors via conceptor negation"):
  with members:
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "all but the top simple and effective postprocessing for word representations"
    - "a network based end to end trainable task oriented dialogue system"
    - "enriching word vectors with subword information"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "towards universal paraphrastic sentence embeddings"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-8 (induced by source paper "large scale multi domain belief tracking with knowledge sharing"):
  with members:
    - "scalable multi domain dialogue state tracking"
    - "comparative study of cnn and rnn for natural language processing"
    - "neural belief tracker data driven dialogue state tracking"
    - "dialog state tracking a machine reading approach using memory network"
    - "incremental lstm based dialog state tracker"
    - "adam a method for stochastic optimization"
    - "a convolutional neural network for modelling sentences"

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)