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: "flexible and scalable state tracking framework for goal oriented dialogue systems"
  Abstract: Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain. This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained. It is also harder to extend such dialogue systems to different and multiple domains. The dialogue state tracker in conventional dialogue systems is one such component - it is usually designed to fit a well-defined application domain. For example, it is common for a state variable to be a categorical distribution over a manually-predefined set of entities (Henderson et al., 2013), resulting in an inflexible and hard-to-extend dialogue system. In this paper, we propose a new approach for dialogue state tracking that can generalize well over multiple domains without incorporating any domain-specific knowledge. Under this framework, discrete dialogue state variables are learned independently and the information of a predefined set of possible values for dialogue state variables is not required. Furthermore, it enables adding arbitrary dialogue context as features and allows for multiple values to be associated with a single state variable. These characteristics make it much easier to expand the dialogue state space. We evaluate our framework using the widely used dialogue state tracking challenge data set (DSTC2) and show that our framework yields competitive results with other state-of-the-art results despite incorporating little domain knowledge. We also show that this framework can benefit from widely available external resources such as pre-trained word embeddings.

Connected through hyperedge HE-1 (induced by source paper "dialogact2vec towards end to end dialogue agent by multi task representation learning"):
  with members:
    - "multi task learning for continuous control"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "global locally self attentive dialogue state tracker"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "learning personalized end to end goal oriented dialog"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "an end to end dialogue state tracking system with machine reading comprehension and wide deep classification"):
  with members:
    - "dialog state tracking a neural reading comprehension approach"
    - "bert dst scalable end to end dialogue state tracking with bidirectional encoder representations from transformer"
    - "find or classify dual strategy for slot value predictions on multi domain dialog state tracking"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"

Connected through hyperedge HE-4 (induced by source paper "bert dst scalable end to end dialogue state tracking with bidirectional encoder representations from transformer"):
  with members:
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "building a conversational agent overnight with dialogue self play"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable multi domain dialogue state tracking"
    - "attention is all you need"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-5 (induced by source paper "sumbt slot utterance matching for universal and scalable belief tracking"):
  with members:
    - "fully statistical neural belief tracking"
    - "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"
    - "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 "hyst a hybrid approach for flexible and accurate dialogue state tracking"):
  with members:
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "scalable multi domain dialogue state tracking"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"

Connected through hyperedge HE-7 (induced by source paper "teacher student framework enhanced multi domain dialogue generation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "scalable multi domain dialogue state tracking"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-8 (induced by source paper "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"):
  with members:
    - "dialog state tracking a neural reading comprehension approach"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "transferable multi domain state generator for 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"
    - "towards zero shot frame semantic parsing for domain scaling"

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.AI(Artificial Intelligence)