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: "scalable multi domain dialogue state tracking"
  Abstract: Dialogue state tracking (DST) is a key component of task-oriented dialogue systems. DST estimates the user's goal at each user turn given the interaction until then. State of the art approaches for state tracking rely on deep learning methods, and represent dialogue state as a distribution over all possible slot values for each slot present in the ontology. Such a representation is not scalable when the set of possible values are unbounded (e.g., date, time or location) or dynamic (e.g., movies or usernames). Furthermore, training of such models requires labeled data, where each user turn is annotated with the dialogue state, which makes building models for new domains challenging. In this paper, we present a scalable multi-domain deep learning based approach for DST. We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge. Restricting these candidate sets to be bounded in size addresses the problem of slot-scalability. Furthermore, by leveraging the slot-independent architecture and transfer learning, we show that our proposed approach facilitates quick adaptation to new domains.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive dialog state tracking"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "dialog state tracking a neural reading comprehension approach"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-2 (induced by source paper "hierarchical context enhanced multi domain dialogue system for multi domain task completion"):
  with members:
    - "structured fusion networks for dialog"
    - "convlab multi domain end to end dialog system platform"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "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"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-3 (induced by source paper "schema guided dialogue state tracking task at dstc8"):
  with members:
    - "fine tuning bert for schema guided zero shot dialogue state tracking"
    - "goal oriented multi task bert based dialogue state tracker"
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "robust zero shot cross domain slot filling with example values"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "transferable multi domain state generator for task oriented dialogue systems"

Connected through hyperedge HE-4 (induced by source paper "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-5 (induced by source paper "retrieval based goal oriented dialogue generation"):
  with members:
    - "scaling multi domain dialogue state tracking via query reformulation"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "on extractive and abstractive neural document summarization with transformer language models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "towards universal dialogue state tracking"
    - "a strong baseline for question relevancy ranking"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"

Connected through hyperedge HE-6 (induced by source paper "transferable multi domain state generator for task oriented dialogue systems"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "the natural language decathlon multitask learning as question answering"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "neural approaches to conversational ai"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"

Connected through hyperedge HE-7 (induced by source paper "scalable and accurate dialogue state tracking via hierarchical sequence generation"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "dialog state tracking a neural reading comprehension approach"
    - "on the convergence of adam and beyond"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "sgm sequence generation model for multi label classification"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-8 (induced by source paper "dialog state tracking a neural reading comprehension approach"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "linguistic knowledge and transferability of contextual representations"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"

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)