human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "manifestation of image contrast in deep networks"):
  Abstract: Contrast is subject to dramatic changes across the visual field, depending on the source of light and scene configurations. Hence, the human visual system has evolved to be more sensitive to contrast than absolute luminance. This feature is equally desired for machine vision: the ability to recognise patterns even when aspects of them are transformed due to variation in local and global contrast. In this work, we thoroughly investigate the impact of image contrast on prominent deep convolutional networks, both during the training and testing phase. The results of conducted experiments testify to an evident deterioration in the accuracy of all state-of-the-art networks at low-contrast images. We demonstrate that "contrast-augmentation" is a sufficient condition to endow a network with invariance to contrast. This practice shows no negative side effects, quite the contrary, it might allow a model to refrain from other illuminance related over-fittings. This ability can also be achieved by a short fine-tuning procedure, which opens new lines of investigation on mechanisms involved in two networks whose weights are over 99.9% correlated, yet astonishingly produce utterly different outcomes. Our further analysis suggests that the optimisation algorithm is an influential factor, however with a significantly lower effect; and while the choice of an architecture manifests a negligible impact on this phenomenon, the first layers appear to be more critical.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "how is contrast encoded in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "paradox in deep neural networks similar yet different while different yet similar")
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "comparing deep neural networks against humans object recognition when the signal gets weaker":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomaly detection in video data based on probabilistic latent space models")
  Member "deeptest automated testing of deep neural network driven autonomous cars":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "importance driven deep learning system testing")

Question: Please tell me which class the source paper 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)