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 the robustness of convolutional neural networks to internal architecture and weight perturbations"
  Abstract: Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the robustness of convolutional neural networks to perturbations to the internal weights and architecture of the network itself. We show that convolutional networks are surprisingly robust to a number of internal perturbations in the higher convolutional layers but the bottom convolutional layers are much more fragile. For instance, Alexnet shows less than a 30% decrease in classification performance when randomly removing over 70% of weight connections in the top convolutional or dense layers but performance is almost at chance with the same perturbation in the first convolutional layer. Finally, we suggest further investigations which could continue to inform the robustness of convolutional networks to internal perturbations.

Connected through hyperedge HE-1 (induced by source paper "paradox in deep neural networks similar yet different while different yet similar"):
  with members:
    - "manifestation of image contrast in deep networks"
    - "visual interpretability for deep learning a survey"
    - "generalisation in humans and deep neural networks"
    - "how is contrast encoded in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpretable convolutional neural networks"
    - "practical black box attacks against machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "adversarial initialization when your network performs the way i want"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "wolf in sheep s clothing the downscaling attack against deep learning applications"
    - "adversarial dropout for supervised and semi supervised learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "security risks in deep learning implementations"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "when single event upset meets deep neural networks observations explorations and remedies"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "learning phase competition for traffic signal control"
    - "intrinsic image transformation via scale space decomposition"
    - "neural color transfer between images"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

Connected through hyperedge HE-4 (induced by source paper "stabilizing inputs to approximated nonlinear functions for inference with homomorphic encryption in deep neural networks"):
  with members:
    - "chet compiler and runtime for homomorphic evaluation of tensor programs"
    - "cryptodl deep neural networks over encrypted data"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep learning with limited numerical precision"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "network in network"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "gradient band based adversarial training for generalized attack immunity of a3c path finding"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards proving the adversarial robustness of deep neural networks"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "extending defensive distillation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "defense against universal adversarial perturbations"

Connected through hyperedge HE-6 (induced by source paper "a neural spiking approach compared to deep feedforward networks on stepwise pixel erasement"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "towards biologically plausible deep learning"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "adadelta an adaptive learning rate method"
    - "multi column deep neural networks for image classification"

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)