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: "distorted representation space characterization through backpropagated gradients"
  Abstract: In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms. We demonstrate the utility of such gradients in applications including perceptual image quality assessment and out-of-distribution classification. The applications are chosen to validate the effectiveness of gradients as features when the test image distribution is distorted from the train image distribution. In both applications, the proposed gradient based features outperform activation features. In image quality assessment, the proposed approach is compared with other state of the art approaches and is generally the top performing method on TID 2013 and MULTI-LIVE databases in terms of accuracy, consistency, linearity, and monotonic behavior. Finally, we analyze the effect of regularization on gradients using CURE-TSR dataset for out-of-distribution classification.

Connected through hyperedge HE-1 (induced by source paper "challenging environments for traffic sign detection reliability assessment under inclement conditions"):
  with members:
    - "traffic sign detection under challenging conditions a deeper look into performance variations and spectral characteristics"
    - "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "cure or challenging unreal and real environments for object recognition"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "traffic sign detection under challenging conditions a deeper look into performance variations and spectral characteristics"):
  with members:
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "cure or challenging unreal and real environments for object recognition"
    - "knowledge based recurrent attentive neural network for traffic sign detection"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

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)