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: "speech commands a dataset for limited vocabulary speech recognition"
  Abstract: Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. Discusses why this task is an interesting challenge, and why it requires a specialized dataset that is different from conventional datasets used for automatic speech recognition of full sentences. Suggests a methodology for reproducible and comparable accuracy metrics for this task. Describes how the data was collected and verified, what it contains, previous versions and properties. Concludes by reporting baseline results of models trained on this dataset.

Connected through hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation"):
  with members:
    - "convolutional networks with dense connectivity"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "implicit semantic data augmentation for deep networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bertscore evaluating text generation with bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "natural adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "π nets deep polynomial neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "polygan high order polynomial generators"
    - "on the convergence of adam and beyond"
    - "semantic image synthesis with spatially adaptive normalization"
    - "on the expressive power of deep polynomial neural networks"
    - "adversarial training of partially invertible variational autoencoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "residual networks of residual networks multilevel residual networks"

Connected through hyperedge HE-3 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "discovering neural wirings"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "butterfly transform an efficient fft based neural architecture design"
    - "green ai"

Connected through hyperedge HE-4 (induced by source paper "entangled watermarks as a defense against model extraction"):
  with members:
    - "similarity of neural network representations revisited"
    - "high fidelity extraction of neural network models"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "energy and policy considerations for deep learning in nlp"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-5 (induced by source paper "mse optimal neural network initialization via layer fusion"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "channel charting locating users within the radio environment using channel state information"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-6 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"
    - "sequence discriminative training for deep learning based acoustic keyword spotting"

Connected through hyperedge HE-7 (induced by source paper "performance oriented neural architecture search"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quenn quantization engine for low power neural networks"
    - "streaming end to end speech recognition for mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "hello edge keyword spotting on microcontrollers"
    - "optimal dnn primitive selection with partitioned boolean quadratic programming"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "smash one shot model architecture search through hypernetworks"

Connected through hyperedge HE-8 (induced by source paper "certification of semantic perturbations via randomized smoothing"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient formal safety analysis of neural networks"
    - "certified defenses against adversarial examples"

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)