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: "toward scalable neural dialogue state tracking model"
  Abstract: The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + \# slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by $35\%$ on average, while preserving performance of belief state tracking, by $97.38\%$ on turn request and $88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.

Connected through hyperedge HE-1 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "dialog state tracking with reinforced data augmentation"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "user modeling for task oriented dialogues"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "dialog state tracking with reinforced data augmentation"):
  with members:
    - "improving dialogue state tracking by discerning the relevant context"
    - "deep communicating agents for abstractive summarization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "reinforced co training"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "towards universal dialogue state tracking"
    - "data augmentation for spoken language understanding via joint variational generation"

Connected through hyperedge HE-5 (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"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "diverse few shot text classification with multiple metrics"

Connected through hyperedge HE-6 (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-7 (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"

Connected through hyperedge HE-8 (induced by source paper "efficient dialogue state tracking by selectively overwriting memory"):
  with members:
    - "non autoregressive dialog state tracking"
    - "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"
    - "scalable and accurate dialogue state tracking via hierarchical sequence generation"
    - "find or classify dual strategy for slot value predictions on multi domain dialog state tracking"
    - "transferable multi domain state generator for task oriented dialogue systems"

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)