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: "optimized pre compensating compression"
  Abstract: In imaging systems, following acquisition, an image/video is transmitted or stored and eventually presented to human observers using different and often imperfect display devices. While the resulting quality of the output image may severely be affected by the display, this degradation is usually ignored in the preceding compression. In this paper, we model the sub-optimality of the display device as a known degradation operator applied on the decompressed image/video. We assume the use of a standard compression path, and augment it with a suitable pre-processing procedure, providing a compressed signal intended to compensate the degradation without any post-filtering. Our approach originates from an intricate rate-distortion problem, optimizing the modifications to the input image/video for reaching best end-to-end performance. We address this seemingly computationally intractable problem using the alternating direction method of multipliers approach, leading to a procedure in which a standard compression technique is iteratively applied. We demonstrate the proposed method for adjusting HEVC image/video compression to compensate post-decompression visual effects due to a common type of displays. Particularly, we use our method to reduce motion-blur perceived while viewing video on LCD devices. The experiments establish our method as a leading approach for preprocessing high bit-rate compression to counterbalance a post-decompression degradation.

Connected through hyperedge HE-1 (induced by source paper "benefiting from duplicates of compressed data shift based holographic compression of images"):
  with members:
    - "system aware compression"
    - "compression for multiple reconstructions"
    - "holographic sensing"
    - "restoration by compression"

Connected through hyperedge HE-2 (induced by source paper "system aware compression"):
  with members:
    - "restoration by compression"
    - "the little engine that could regularization by denoising red"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "postprocessing of compressed images via sequential denoising"
    - "poisson inverse problems by the plug and play scheme"

Connected through hyperedge HE-3 (induced by source paper "compression for multiple reconstructions"):
  with members:
    - "system aware compression"
    - "perceptually optimized image rendering"
    - "distributed functional scalar quantization simplified"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

Connected through hyperedge HE-4 (induced by source paper "learning a virtual codec based on deep convolutional neural network to compress image"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "fast adaptive bilateral filtering"
    - "multiple description convolutional neural networks for image compression"
    - "deep multiple description coding by learning scalar quantization"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "an end to end compression framework based on convolutional neural networks"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "learning convolutional networks for content weighted image 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.MM(Multimedia)