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: "fine grained recognition in the noisy wild sensitivity analysis of convolutional neural networks approaches"
  Abstract: In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition. In particular, we answer the following questions (1) how sensitive are CNNs with respect to image transformations encountered during wild image capture?; (2) how can we predict CNN sensitivity?; and (3) can we increase the robustness of CNNs with respect to image degradations? To answer the first question, we provide an extensive empirical sensitivity analysis of commonly used CNN architectures (AlexNet, VGG19, GoogleNet) across various types of image degradations. This allows for predicting CNN performance for new domains comprised by images of lower quality or captured from a different viewpoint. We also show how the sensitivity of CNN outputs can be predicted for single images. Furthermore, we demonstrate that input layer dropout or pre-filtering during test time only reduces CNN sensitivity for high levels of degradation. #R##N#Experiments for fine-grained recognition tasks reveal that VGG19 is more robust to severe image degradations than AlexNet and GoogleNet. However, small intensity noise can lead to dramatic changes in CNN performance even for VGG19.

Connected through hyperedge HE-1 (induced by source paper "deepcorrect correcting dnn models against image distortions"):
  with members:
    - "deflecting adversarial attacks with pixel deflection"
    - "on classification of distorted images with deep convolutional neural networks"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "practical black box attacks against machine learning"
    - "how image degradations affect deep cnn based face recognition"
    - "a neural approach to blind motion deblurring"

Connected through hyperedge HE-2 (induced by source paper "it gan do better gan based detection of objects on images with varying quality"):
  with members:
    - "a robust learning approach to domain adaptive object detection"
    - "deepcorrect correcting dnn models against image distortions"
    - "focal loss for dense object detection"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "perceptual generative adversarial networks for small object detection"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "examining the impact of blur on recognition by convolutional networks"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "a possible reason for why data driven beats theory driven computer vision"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "leverage eye movement data for saliency modeling invariance analysis and a robust new model"
    - "effects of degradations on deep neural network architectures"
    - "yolov3 an incremental improvement"
    - "generalisation in humans and deep neural networks"
    - "the elephant in the room"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "active control of camera parameters for object detection algorithms"

Connected through hyperedge HE-4 (induced by source paper "why do deep convolutional networks generalize so poorly to small image transformations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "studying invariances of trained convolutional neural networks"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "exploring the space of black box attacks on deep neural networks"
    - "the elephant in the room"

Connected through hyperedge HE-5 (induced by source paper "are you tampering with my data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "potrojan powerful neural level trojan designs in deep learning models"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "peernets exploiting peer wisdom against adversarial attacks"

Connected through hyperedge HE-6 (induced by source paper "past present and future approaches using computer vision for animal re identification from camera trap data"):
  with members:
    - "face recognition primates in the wild"
    - "deep learning object detection methods for ecological camera trap data"
    - "dynamic routing between capsules"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "ssd single shot multibox detector"
    - "automated visual fin identification of individual great white sharks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "butterfly effect bidirectional control of classification performance by small additive perturbation"):
  with members:
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "learning universal adversarial perturbations with generative models"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "efficient defenses against adversarial attacks"
    - "fast feature fool a data independent approach to universal adversarial perturbations"

Connected through hyperedge HE-8 (induced by source paper "can the early human visual system compete with deep neural networks"):
  with members:
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "residual attention network for image classification"
    - "understanding how image quality affects deep neural networks"
    - "deep networks can resemble human feed forward vision in invariant object recognition"
    - "25 years of cnns can we compare to human abstraction capabilities"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)