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: "accelerating deep learning with shrinkage and recall"
  Abstract: Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data sets. Results show that the speedup using shrinking Deep Learning with recall (sDLr) can reach more than 2.0 while still giving competitive classification performance.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks for failure prediction"):
  with members:
    - "a cost sensitive deep belief network for imbalanced classification"
    - "minimal support vector machine"
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "regularized singular value decomposition and application to recommender system"
    - "camera style adaptation for person re identification"
    - "controllable generative adversarial network"
    - "a bayesian data augmentation approach for learning deep models"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "manufacturing dispatching using reinforcement and transfer learning"):
  with members:
    - "minimal support vector machine"
    - "regularized singular value decomposition and application to recommender system"
    - "cross domain transfer in reinforcement learning using target apprentice"
    - "a harmonic mean linear discriminant analysis for robust image classification"
    - "kernel alignment inspired linear discriminant analysis"
    - "analysis and modeling of social influence in high performance computing workloads"

Connected through hyperedge HE-3 (induced by source paper "remaining useful life estimation using functional data analysis"):
  with members:
    - "two birds with one network unifying failure event prediction and time to failure modeling"
    - "functional multi layer perceptron a non linear tool for functional data analysis"

Connected through hyperedge HE-4 (induced by source paper "regularized singular value decomposition and application to recommender system"):
  with members:
    - "minimal support vector machine"
    - "a harmonic mean linear discriminant analysis for robust image classification"
    - "kernel alignment inspired linear discriminant analysis"
    - "analysis and modeling of social influence in high performance computing workloads"

Connected through hyperedge HE-5 (induced by source paper "minimal support vector machine"):
  with members:
    - "kernel alignment inspired linear discriminant analysis"
    - "a closed form solution to multi view low rank regression"
    - "analysis and modeling of social influence in high performance computing workloads"

Connected through hyperedge HE-6 (induced by source paper "what does fault tolerant deep learning need from mpi"):
  with members:
    - "parle parallelizing stochastic gradient descent"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "fault tolerant frequent pattern mining"
    - "adaptive neuron apoptosis for accelerating deep learning on large scale systems"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"
    - "asynchronous parallel stochastic gradient descent a numeric core for scalable distributed machine learning algorithms"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"

Connected through hyperedge HE-7 (induced by source paper "a harmonic mean linear discriminant analysis for robust image classification"):
  with members:
    - "kernel alignment inspired linear discriminant analysis"
    - "a closed form solution to multi view low rank regression"
    - "analysis and modeling of social influence in high performance computing workloads"

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)