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: "saliency guided complexity control for hevc decoding"
  Abstract: The latest High Efficiency Video Coding (HEVC) standard significantly improves coding efficiency over its previous video coding standards. The expense of such improvement is enormous computational complexity, from both encoding and decoding sides. Since computational capability and power capacity are diverse across portable devices, it is necessary to reduce decoding complexity to a target with tolerable quality loss, so called complexity control. This paper proposes a saliency-guided complexity control (SGCC) approach for HEVC decoding, which reduces the decoding complexity to the target with minimal perceptual quality loss. First, we establish the SGCC formulation to minimize perceptual quality loss at the constraint on reduced decoding complexity, which is achieved via disabling deblocking filter (DF) and simplifying motion compensation (MC) of some non-salient coding tree units (CTUs). One important component in this formulation is the modeled relationship between decoding complexity reduction and DF disabling/MC simplification, which determines the control accuracy of our approach. Another component is the modeled relationship between quality loss and DF disabling/MC simplification, responsible for optimizing perceptual quality. By solving the SGCC formulation for a given target complexity, we can obtain the DF and MC settings of each CTU, and then decoding complexity can be reduced to the target. Finally, the experimental results validate the effectiveness of our SGCC approach, from the aspects of control performance, complexity-distortion performance, and fluctuation of quality loss and subjective quality.

Connected through hyperedge HE-1 (induced by source paper "mfqe 2 0 a new approach for multi frame quality enhancement on compressed video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "multi frame quality enhancement for compressed video"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "detail revealing deep video super resolution"
    - "end to end learning of video super resolution with motion compensation"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-2 (induced by source paper "enhancing quality for hevc compressed videos"):
  with members:
    - "reducing complexity of hevc a deep learning approach"
    - "in datacenter performance analysis of a tensor processing unit"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "mathbf d 3 deep dual domain based fast restoration of jpeg compressed images"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "compression artifacts reduction by a deep convolutional network"

Connected through hyperedge HE-3 (induced by source paper "multi frame quality enhancement for compressed video"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "end to end learning of video super resolution with motion compensation"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "deep residual learning for image recognition"
    - "compression artifacts reduction by a deep convolutional network"

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)