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: "onenet joint domain intent slot prediction for spoken language understanding"
  Abstract: In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according to the semantic frames of the predicted domain. The pipeline approach, however, has some disadvantages: error propagation and lack of information sharing. To address these issues, we present a unified neural network that jointly performs domain, intent, and slot predictions. Our approach adopts a principled architecture for multitask learning to fold in the state-of-the-art models for each task. With a few more ingredients, e.g. orthography-sensitive input encoding and curriculum training, our model delivered significant improvements in all three tasks across all domains over strong baselines, including one using oracle prediction for domain detection, on real user data of a commercial personal assistant.

Connected through hyperedge HE-1 (induced by source paper "hierarchical context enhanced multi domain dialogue system for multi domain task completion"):
  with members:
    - "structured fusion networks for dialog"
    - "convlab multi domain end to end dialog system platform"
    - "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"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "scalable multi domain dialogue state tracking"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-2 (induced by source paper "towards open intent discovery for conversational text"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi domain adversarial learning for slot filling in spoken language understanding"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (induced by source paper "convlab multi domain end to end dialog system platform"):
  with members:
    - "user modeling for task oriented dialogues"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "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-4 (induced by source paper "improving robustness of task oriented dialog systems"):
  with members:
    - "unsupervised data augmentation for consistency training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "efficient augmentation via data subsampling"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "generating natural language adversarial examples"
    - "constructing unrestricted adversarial examples with generative models"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "bootstrapping nlu models with multi task learning"):
  with members:
    - "learning word vectors for 157 languages"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "enriching word vectors with subword information"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "neural architectures for named entity recognition"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-6 (induced by source paper "iterative delexicalization for improved spoken language understanding"):
  with members:
    - "coupled representation learning for domains intents and slots in spoken language understanding"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "robust spoken language understanding via paraphrasing"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "end to end task completion neural dialogue systems"
    - "language to logical form with neural attention"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

Connected through hyperedge HE-7 (induced by source paper "spoken language intent detection using confusion2vec"):
  with members:
    - "confusion2vec towards enriching vector space word representations with representational ambiguities"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "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-8 (induced by source paper "incremental online spoken language understanding"):
  with members:
    - "spoken language intent detection using confusion2vec"
    - "towards end to end spoken language understanding"
    - "investigating speech features for continuous turn taking prediction using lstms"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

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)