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: "deep speech 2 end to end speech recognition in english and mandarin"
  Abstract: We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

Connected through hyperedge HE-1 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection"):
  with members:
    - "proving the lottery ticket hypothesis pruning is all you need"
    - "pruning from scratch"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "searching for mobilenetv3"
    - "data independent neural pruning via coresets"
    - "legr filter pruning via learned global ranking"
    - "what s hidden in a randomly weighted neural network"
    - "provable filter pruning for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks"):
  with members:
    - "weight normalization based quantization for deep neural network compression"
    - "knowledge distillation for optimization of quantized deep neural networks"
    - "quantization networks"
    - "swalp stochastic weight averaging in low precision training"
    - "empirical analysis of knowledge distillation technique for optimization of quantized deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning robust and multilingual speech representations"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "on variational bounds of mutual information"
    - "self training for end to end speech recognition"
    - "libri light a benchmark for asr with limited or no supervision"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-4 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "meshcnn a network with an edge"
    - "learning neural pde solvers with convergence guarantees"
    - "a deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials"
    - "one shot generation of near optimal topology through theory driven machine learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural networks for topology optimization"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-5 (induced by source paper "enlarging discriminative power by adding an extra class in unsupervised domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "co regularized alignment for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "run time deep model multiplexing"):
  with members:
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "deep feature compression for collaborative object detection"
    - "mnasnet platform aware neural architecture search for mobile"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-7 (induced by source paper "efficient training of deep convolutional neural networks by augmentation in embedding space"):
  with members:
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "a meta learning approach for custom model training"
    - "an analysis of deep neural network models for practical applications"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "group equivariant convolutional networks"
    - "deep residual learning for image recognition"
    - "learning to see by moving"

Connected through hyperedge HE-8 (induced by source paper "a data efficient end to end spoken language understanding architecture"):
  with members:
    - "hybrid neural models for sequence modelling the best of three worlds"
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "towards end to end spoken language understanding"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"
    - "asr error management for improving spoken language understanding"
    - "listen and translate a proof of concept for end to end speech to text translation"

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)