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: "cnn fixations an unraveling approach to visualize the discriminative image regions"
  Abstract: Deep convolutional neural networks (CNN) have revolutionized various fields of vision research and have seen unprecedented adoption for multiple tasks such as classification, detection, captioning, etc. However, they offer little transparency into their inner workings and are often treated as black boxes that deliver excellent performance. In this work, we aim at alleviating this opaqueness of CNNs by providing visual explanations for the network's predictions. Our approach can analyze variety of CNN based models trained for vision applications such as object recognition and caption generation. Unlike existing methods, we achieve this via unraveling the forward pass operation. Proposed method exploits feature dependencies across the layer hierarchy and uncovers the discriminative image locations that guide the network's predictions. We name these locations CNN-Fixations, loosely analogous to human eye fixations. #R##N#Our approach is a generic method that requires no architectural changes, additional training or gradient computation and computes the important image locations (CNN Fixations). We demonstrate through a variety of applications that our approach is able to localize the discriminative image locations across different network architectures, diverse vision tasks and data modalities.

Connected through hyperedge HE-1 (induced by source paper "a tour of convolutional networks guided by linear interpreters"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "understanding deep architectures by visual summaries"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-2 (induced by source paper "exploring frequency domain interpretation of convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "a tour of convolutional networks guided by linear interpreters"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "autoaugment learning augmentation policies from data"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

Connected through hyperedge HE-3 (induced by source paper "oct fingerprints resilience to presentation attacks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "universal material translator towards spoof fingerprint generalization"
    - "livdet in action fingerprint liveness detection competition 2019"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "raspireader open source fingerprint reader"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "ask acquire and attack data free uap generation using class impressions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "nag network for adversary generation"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "practical black box attacks against machine learning"
    - "improved techniques for training gans"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"

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)