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: "mckernel approximate kernel expansions in log linear time through randomization"
  Abstract: McKernel introduces a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. Based on Random Kitchen Sinks [Rahimi and Recht 2007], we provide a C++ library for Large-scale Machine Learning. It contains a CPU optimized implementation of the algorithm in [Le et al. 2013], that allows the computation of approximated kernel expansions in log-linear time. The algorithm requires to compute the product of matrices Walsh Hadamard. A cache friendly Fast Walsh Hadamard that achieves compelling speed and outperforms current state-of-the-art methods has been developed. McKernel establishes the foundation of a new architecture of learning that allows to obtain large-scale non-linear classification combining lightning kernel expansions and a linear classifier. It travails in the mini-batch setting working analogously to Neural Networks. We show the validity of our method through extensive experiments on MNIST and FASHION-MNIST [Xiao et al. 2017].

Connected through hyperedge HE-1 (induced by source paper "doctor of crosswise reducing over parametrization in neural networks"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "high resolution deep convolutional generative adversarial networks"):
  with members:
    - "video to video synthesis"
    - "faceshop deep sketch based face image editing"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "smit stochastic multi label image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "deep appearance models for face rendering"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-3 (induced by source paper "segmentation of instances by hashing"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "towards accurate multi person pose estimation in the wild"
    - "rethinking atrous convolution for semantic image segmentation"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "improved techniques for training gans"

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.LG(Machine Learning)