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: "deep residual learning and pdes on manifold"
  Abstract: In this paper, we formulate the deep residual network (ResNet) as a control problem of transport equation. In ResNet, the transport equation is solved along the characteristics. Based on this observation, deep neural network is closely related to the control problem of PDEs on manifold. We propose several models based on transport equation and Hamilton-Jacobi equation. The discretization of these PDEs on point cloud is also discussed.

Connected through hyperedge HE-1 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"

Connected through hyperedge HE-2 (induced by source paper "graph interpolating activation improves both natural and robust accuracies in data efficient deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "mathematical analysis of adversarial attacks"
    - "deep neural nets with interpolating function as output activation"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "laplacian smoothing gradient descent"

Connected through hyperedge HE-3 (induced by source paper "sparsity meets robustness channel pruning for the feynman kac formalism principled robust deep neural nets"):
  with members:
    - "adversarial robustness vs model compression or both"
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarially trained model compression when robustness meets efficiency"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-4 (induced by source paper "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "mathematical analysis of adversarial attacks"
    - "deep neural nets with interpolating function as output activation"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "forward stability of resnet and its variants"):
  with members:
    - "deep neural nets with interpolating function as output activation"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "mathematics of deep learning"
    - "visualizing the loss landscape of neural nets"
    - "deep relaxation partial differential equations for optimizing deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "stochastic training of residual networks a differential equation viewpoint"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "laplacian smoothing gradient descent"
    - "learning across scales a multiscale method for convolution neural networks"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "don t decay the learning rate increase the batch size"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-7 (induced by source paper "beyond finite layer neural networks bridging deep architectures and numerical differential equations"):
  with members:
    - "the reversible residual network backpropagation without storing activations"
    - "squeeze and excitation networks"
    - "opening the black box of deep neural networks via information"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "shake shake regularization"
    - "diracnets training very deep neural networks without skip connections"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-8 (induced by source paper "a flow model of neural networks"):
  with members:
    - "maximum principle based algorithms for deep learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional 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.IT(Information Theory)