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: "a comparative measurement study of deep learning as a service framework"
  Abstract: Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of data openly accessible, a growing number of DL frameworks, and a selection of affordable hardware devices. However, no single DL framework, to date, dominates, making the selection of DL frameworks overwhelming. This paper takes a holistic approach to conduct empirical comparison and analysis of four representative DL frameworks with three unique contributions. First, we show that for a specific DL framework, different configurations of its hyper-parameters may have a significant impact on performance. Second, this study is the first to identify the opportunities for improving the runtime performance and accuracy of DL frameworks by configuring computing libraries and tuning individual and multiple hyper-parameters. Third, we conduct a comparative measurement study on the resource consumption patterns and their performance implications, including CPU and memory usage, and their correlations to hyper-parameters. We argue that this study provides in-depth empirical comparison and analysis of DL frameworks, and offers practical guidance for service providers to deploying and delivering DL as a Service and for application developers and DLaaS consumers to select the right DL frameworks for the right DL workloads.

Connected through hyperedge HE-1 (induced by source paper "denoising and verification cross layer ensemble against black box adversarial attacks"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial examples in deep learning characterization and divergence"
    - "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "snapshot ensembles train 1 get m for free"

Connected through hyperedge HE-2 (induced by source paper "demystifying learning rate polices for high accuracy training of deep neural networks"):
  with members:
    - "super convergence very fast training of residual networks using large learning rates"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "channel pruning for accelerating very deep neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "no more pesky learning rate guessing games"

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.PF(Performance)