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: "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"
  Abstract: In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, and object orientation. We focus on platforms that are commonly used by the public to better understand their real-world performances. To assess the variation in recognition performance, we perform a controlled experiment by changing the acquisition conditions one at a time. We use three smartphones, one DSLR, and one webcam to capture side views and overhead views of objects in a living room, an office, and photo studio setups. Moreover, we introduce a framework to estimate the recognition performance with respect to backgrounds and orientations. In this framework, we utilize both handcrafted features based on color, texture, and shape characteristics and data-driven features obtained from deep neural networks. Experimental results show that deep learning-based image representations can estimate the recognition performance variation with a Spearman's rank-order correlation of 0.94 under multifarious acquisition conditions.

Connected through hyperedge HE-1 (induced by source paper "challenging environments for traffic sign detection reliability assessment under inclement conditions"):
  with members:
    - "distorted representation space characterization through backpropagated gradients"
    - "traffic sign detection under challenging conditions a deeper look into performance variations and spectral characteristics"
    - "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:
    - "distorted representation space characterization through backpropagated gradients"
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "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)