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: "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
  Abstract: This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy.

Connected through hyperedge HE-1 (induced by source paper "efficient intent detection with dual sentence encoders"):
  with members:
    - "q8bert quantized 8bit bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "training neural response selection for task oriented dialogue systems"
    - "pretraining methods for dialog context representation learning"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a repository of conversational datasets"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"

Connected through hyperedge HE-2 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "roberta a robustly optimized bert pretraining approach"
    - "joint slot filling and intent detection via capsule neural networks"
    - "exploring neural methods for parsing discourse representation structures"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic parsing for task oriented dialog using hierarchical representations"

Connected through hyperedge HE-3 (induced by source paper "diversity density and homogeneity quantitative characteristic metrics for text collections"):
  with members:
    - "casa nlu context aware self attentive natural language understanding for task oriented chatbots"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "computationally efficient ner taggers with combined embeddings and constrained decoding"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep active learning for named entity recognition"
    - "semi supervised sequence tagging with bidirectional language models"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "neunets an automated synthesis engine for neural network design"):
  with members:
    - "alphax exploring neural architectures with deep neural networks and monte carlo tree search"
    - "practical deep learning architecture optimization"
    - "massively parallel hyperparameter tuning"
    - "regularized evolution for image classifier architecture search"
    - "incremental training of deep convolutional neural networks"
    - "efficient image dataset classification difficulty estimation for predicting deep learning accuracy"
    - "darts differentiable architecture search"
    - "neural network acceptability judgments"

Connected through hyperedge HE-6 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text"):
  with members:
    - "poincare wasserstein autoencoder"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "privacy preserving active learning on sensitive data for user intent classification"
    - "generalised differential privacy for text document processing"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "poincar e glove hyperbolic word embeddings"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-7 (induced by source paper "a closer look at feature space data augmentation for few shot intent classification"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "data augmentation with atomic templates for spoken language understanding"
    - "data augmentation for low resource sentiment analysis using generative adversarial networks"
    - "few shot learning with meta metric learners"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "active learning for new domains in natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "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)