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: "multi task learning for joint language understanding and dialogue state tracking"
  Abstract: This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.

Connected through hyperedge HE-1 (induced by source paper "guessing state tracking for visual dialogue"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "visual dialogue state tracking for question generation"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "an active information seeking model for goal oriented vision and language tasks"
    - "beyond task success a closer look at jointly learning to see ask and guesswhat"

Connected through hyperedge HE-2 (induced by source paper "plato dialogue system a flexible conversational ai research platform"):
  with members:
    - "moss end to end dialog system framework with modular supervision"
    - "collaborative multi agent dialogue model training via reinforcement learning"
    - "convlab multi domain end to end dialog system platform"
    - "huggingface s transformers state of the art natural language processing"
    - "nemo a toolkit for building ai applications using neural modules"
    - "adversarial learning of task oriented neural dialog models"
    - "detecting offensive content in open domain conversations using two stage semi supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"

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 "fine tuning bert for schema guided zero shot dialogue state tracking"):
  with members:
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "zero shot adaptive transfer for conversational language understanding"
    - "building a conversational agent overnight with dialogue self play"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"

Connected through hyperedge HE-5 (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"
    - "learning personalized end to end goal oriented dialog"
    - "flexible and scalable state tracking framework for goal oriented dialogue systems"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"

Connected through hyperedge HE-6 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability"):
  with members:
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "personalization in goal oriented dialog"

Connected through hyperedge HE-7 (induced by source paper "the eighth dialog system technology challenge"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "bert dst scalable end to end dialogue state tracking with bidirectional encoder representations from transformer"
    - "dialog system technology challenge 7"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "convlab multi domain end to end dialog system platform"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "zero shot user intent detection via capsule neural networks"

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)