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: "user modeling for task oriented dialogues"
  Abstract: We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first encodes the initial user goal and system turns into fixed length representations using Recurrent Neural Networks (RNN). It then encodes the dialogue history using another RNN layer. At each turn, user responses are decoded from the hidden representations of the dialogue level RNN. This hierarchical user simulator (HUS) approach allows the model to capture undiscovered parts of the user goal without the need of an explicit dialogue state tracking. We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal. We evaluate the proposed models on movie ticket booking domain by systematically interacting each user simulator with various dialogue system policies trained with different objectives and users.

Connected through hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "domain adaptive dialog generation via meta learning"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "unsupervised dialog structure learning"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"

Connected through hyperedge HE-2 (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"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"):
  with members:
    - "plato dialogue system a flexible conversational ai research platform"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "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"
    - "guided dialog policy learning reward estimation for multi domain task oriented dialog"

Connected through hyperedge HE-4 (induced by source paper "convlab multi domain end to end dialog system platform"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "self imitation learning"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "neural approaches to conversational ai"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"

Connected through hyperedge HE-5 (induced by source paper "guided dialog policy learning reward estimation for multi domain task oriented dialog"):
  with members:
    - "convlab multi domain end to end dialog system platform"
    - "adversarial learning of task oriented neural dialog models"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sentiment adaptive end to end dialog systems"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "modeling multi action policy for task oriented dialogues"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "recurrently controlled recurrent networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "an incremental turn taking model for task oriented dialog systems"):
  with members:
    - "on the convergence of adam and beyond"
    - "recurrent neural networks for dialogue state tracking"
    - "incremental lstm based dialog state tracker"
    - "adam a method for stochastic optimization"

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)