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: "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"
  Abstract: We study mechanisms to characterize how the asymptotic convergence of backpropagation in deep architectures, in general, is related to the network structure, and how it may be influenced by other design choices including activation type, denoising and dropout rate. We seek to analyze whether network architecture and input data statistics may guide the choices of learning parameters and vice versa. Given the broad applicability of deep architectures, this issue is interesting both from theoretical and a practical standpoint. Using properties of general nonconvex objectives (with first-order information), we first build the association between structural, distributional and learnability aspects of the network vis-\`a-vis their interaction with parameter convergence rates. We identify a nice relationship between feature denoising and dropout, and construct families of networks that achieve the same level of convergence. We then derive a workflow that provides systematic guidance regarding the choice of network sizes and learning parameters often mediated4 by input statistics. Our technical results are corroborated by an extensive set of evaluations, presented in this paper as well as independent empirical observations reported by other groups. We also perform experiments showing the practical implications of our framework for choosing the best fully-connected design for a given problem.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive analysis of deep regression"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "lstm a search space odyssey"
    - "learning feature pyramids for human pose estimation"
    - "viraliency pooling local virality"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "deep convolutional neural network design patterns"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "systematic evaluation of convolution neural network advances on the imagenet"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "disturblabel regularizing cnn on the loss layer"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "multi bias non linear activation in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "the incremental multiresolution matrix factorization algorithm"):
  with members:
    - "adapting deep network features to capture psychological representations"
    - "understanding neural networks through deep visualization"
    - "parallel mmf a multiresolution approach to matrix computation"
    - "inverting visual representations with convolutional networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "invariant scattering convolution networks"

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)