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 neural nets with interpolating function as output activation"
  Abstract: We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function. And we propose end-to-end training and testing algorithms for this new architecture. Compared to classical neural nets with softmax function as output activation, the surrogate with interpolating function as output activation combines advantages of both deep and manifold learning. The new framework demonstrates the following major advantages: First, it is better applicable to the case with insufficient training data. Second, it significantly improves the generalization accuracy on a wide variety of networks. The algorithm is implemented in PyTorch, and code will be made publicly available.

Connected through hyperedge HE-1 (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"
    - "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"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "understanding straight through estimator in training activation quantized neural nets"):
  with members:
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "learning one convolutional layer with overlapping patches"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

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 "on robustness of neural ordinary differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "accelerating neural odes with spectral elements"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "analyzing inverse problems with invertible neural networks"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "neural ordinary differential equations"

Connected through hyperedge HE-5 (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"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "stable architectures for deep neural networks"
    - "convolutional neural networks combined with runge kutta methods"

Connected through hyperedge HE-6 (induced by source paper "forward stability of resnet and its variants"):
  with members:
    - "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"
    - "deep residual learning and pdes on manifold"
    - "visualizing the loss landscape of neural nets"
    - "deep relaxation partial differential equations for optimizing deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "blended coarse gradient descent for full quantization of deep neural networks"):
  with members:
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "training quantized nets a deeper understanding"
    - "deep learning with low precision by half wave gaussian quantization"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-8 (induced by source paper "mathematical analysis of adversarial attacks"):
  with members:
    - "deep learning with data dependent implicit activation function"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "black box adversarial attacks with limited queries and information"
    - "sok security and privacy in machine learning"
    - "laplacian smoothing gradient descent"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "synthesizing robust adversarial examples"

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)