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: "protecting jpeg images against adversarial attacks"
  Abstract: As deep neural networks (DNNs) have been integrated into critical systems, several methods to attack these systems have been developed. These adversarial attacks make imperceptible modifications to an image that fool DNN classifiers. We present an adaptive JPEG encoder which defends against many of these attacks. Experimentally, we show that our method produces images with high visual quality while greatly reducing the potency of state-of-the-art attacks. Our algorithm requires only a modest increase in encoding time, produces a compressed image which can be decompressed by an off-the-shelf JPEG decoder, and classified by an unmodified classifier

Connected through hyperedge HE-1 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples"):
  with members:
    - "adversarial robustness toolbox v1 0 0"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "exploiting the inherent limitation of l0 adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-2 (induced by source paper "detecting and correcting adversarial images using image processing operations"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "adef an iterative algorithm to construct adversarial deformations"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-3 (induced by source paper "disentangling adversarial robustness and generalization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "generative adversarial examples"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "on the geometry of adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adef an iterative algorithm to construct adversarial deformations"
    - "adversarial logit pairing"

Connected through hyperedge HE-4 (induced by source paper "exploiting the inherent limitation of l0 adversarial examples"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "adversarial robustness toolbox v0 3 0"
    - "generalizable adversarial examples detection based on bi model decision mismatch"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-5 (induced by source paper "taming the cross entropy loss"):
  with members:
    - "deep learning is robust to massive label noise"
    - "on calibration of modern neural networks"
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"

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.CV(Computer Vision and Pattern Recognition)