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 "transferable recognition aware image processing"):
  Abstract: Recent progress in image recognition has stimulated the deployment of vision systems (e.g. image search engines) at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Meanwhile, existing image processing methods only optimize for better human perception, whereas the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we propose simple approaches to improve machine interpretability of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate transforming model, a process which we show can also be done in an unsupervised manner. Interestingly, the processing model's ability to enhance the recognition performance can transfer when evaluated on different recognition models, even if they are of different architectures, trained on different object categories or even different recognition tasks. This makes the solutions applicable even when we do not have the knowledge about future downstream recognition models, e.g., if we are to upload the processed images to the Internet. We conduct comprehensive experiments on three image processing tasks with two downstream recognition tasks, and confirm our method brings substantial accuracy improvement on both the same recognition model and when transferring to a different one, with minimal or no loss in the image processing quality.

Includes members:
  Member "segmentation aware image denoising without knowing true segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "david dual attentional video deblurring")
  Member "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bridging the gap between computational photography and visual recognition")
  Member "bridging the gap between computational photography and visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "segmentation aware image denoising without knowing true segmentation")
  Member "disentangling features in 3d face shapes for joint face reconstruction and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on learning 3d face morphable model from in the wild images")
  Member "super identity convolutional neural network for face hallucination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "task driven super resolution object detection in low resolution images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "extended feature pyramid network for small object detection")
  Member "residual dense network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")

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)