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 "localized compression applying convolutional neural networks to compressed images"):
  Abstract: We address the challenge of applying existing convolutional neural network (CNN) architectures to compressed images. Existing CNN architectures represent images as a matrix of pixel intensities with a specified dimension; this desired dimension is achieved by downgrading or cropping. Downgrading and cropping are attractive in that the result is also an image; however, an algorithm producing an alternative "compressed" representation could yield better classification performance. This compression algorithm need not be reversible, but must be compatible with the CNN's operations. This problem is thus the counterpart of the well-studied problem of applying compressed CNNs to uncompressed images, which has attracted great interest as CNNs are deployed to size-, weight-, and power- (SWaP)-limited devices. We introduce Localized Compression, a generalization of downgrading in which the original image is divided into blocks and each block is compressed to a smaller size using either sampling- or random-matrix-based techniques. By aligning the size of the compressed blocks with the size of the CNN's convolutional region, localized compression can be made compatible with any CNN architecture. Our experimental results show that Localized Compression results in classification accuracy approximately 1-2% higher than is achieved by downgrading to the equivalent resolution.

Includes members:
  Member "a survey of model compression and acceleration for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "deep compression compressing deep neural networks with pruning trained quantization and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "an exploration of parameter redundancy in deep networks with circulant projections":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low memory neural network training a technical report")
  Member "exploiting linear structure within convolutional networks for efficient evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a tutorial on principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cbir using features derived by deep learning")
  Member "recovery of sparse matrices via matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sub nyquist radar principles and prototypes")

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)