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: "joint online spoken language understanding and language modeling with recurrent neural networks"
  Abstract: Speaker intent detection and semantic slot filling are two critical tasks in spoken language understanding (SLU) for dialogue systems. In this paper, we describe a recurrent neural network (RNN) model that jointly performs intent detection, slot filling, and language modeling. The neural network model keeps updating the intent estimation as word in the transcribed utterance arrives and uses it as contextual features in the joint model. Evaluation of the language model and online SLU model is made on the ATIS benchmarking data set. On language modeling task, our joint model achieves 11.8% relative reduction on perplexity comparing to the independent training language model. On SLU tasks, our joint model outperforms the independent task training model by 22.3% on intent detection error rate, with slight degradation on slot filling F1 score. The joint model also shows advantageous performance in the realistic ASR settings with noisy speech input.

Connected through hyperedge HE-1 (induced by source paper "structured fusion networks for dialog"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "pretraining methods for dialog context representation learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "zero shot dialog generation with cross domain latent actions"
    - "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"
    - "cross domain dialogue policy transfer via simultaneous speech act and slot alignment"

Connected through hyperedge HE-2 (induced by source paper "casa nlu context aware self attentive natural language understanding for task oriented chatbots"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "towards universal dialogue state tracking"
    - "global locally self attentive dialogue state tracker"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "jointly trained sequential labeling and classification by sparse attention neural networks"
    - "convolutional sequence to sequence learning"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "a network based end to end trainable task oriented dialogue system"

Connected through hyperedge HE-3 (induced by source paper "natural language interactions in autonomous vehicles intent detection and slot filling from passenger utterances"):
  with members:
    - "conversational intent understanding for passengers in autonomous vehicles"
    - "key value retrieval networks for task oriented dialogue"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "comparing human and machine errors in conversational speech transcription"
    - "bag of tricks for efficient text classification"
    - "achieving human parity in conversational speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "spoken language intent detection using confusion2vec"):
  with members:
    - "confusion2vec towards enriching vector space word representations with representational ambiguities"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "incremental online spoken language understanding"):
  with members:
    - "spoken language intent detection using confusion2vec"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "towards end to end spoken language understanding"
    - "investigating speech features for continuous turn taking prediction using lstms"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

Connected through hyperedge HE-6 (induced by source paper "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "multi task learning for joint language understanding and dialogue state tracking"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "neural belief tracker data driven dialogue state tracking"
    - "dialog state tracking a machine reading approach using memory network"
    - "end to end memory networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-8 (induced by source paper "zero shot adaptive transfer for conversational language understanding"):
  with members:
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "end to end neural coreference resolution"
    - "zero shot transfer learning for event extraction"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

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)