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: "compression of deep neural networks for image instance retrieval"
  Abstract: Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the dominant approach for generating {\it global image descriptors} for the instance retrieval problem. One major drawback of CNN-based {\it global descriptors} is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware. In this work, we study the problem of neural network model compression focusing on the image instance retrieval task. We study quantization, coding, pruning and weight sharing techniques for reducing model size for the instance retrieval problem. We provide extensive experimental results on the trade-off between retrieval performance and model size for different types of networks on several data sets providing the most comprehensive study on this topic. We compress models to the order of a few MBs: two orders of magnitude smaller than the uncompressed models while achieving negligible loss in retrieval performance.

Connected through hyperedge HE-1 (induced by source paper "improving iot analytics through selective edge execution"):
  with members:
    - "analytics for the internet of things a survey"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "a survey on mobile edge computing the communication perspective"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "mitigate parasitic resistance in resistive crossbar based convolutional neural networks"):
  with members:
    - "fpsa a full system stack solution for reconfigurable reram based nn accelerator architecture"
    - "memristor based deep convolution neural network a case study"
    - "ternary weight networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "2 bit model compression of deep convolutional neural network on asic engine for image retrieval"):
  with members:
    - "compact descriptors for video analysis the emerging mpeg standard"
    - "a survey of model compression and acceleration for deep neural networks"
    - "deep image retrieval learning global representations for image search"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "compressing deep convolutional networks using vector quantization"

Connected through hyperedge HE-4 (induced by source paper "memristor based deep convolution neural network a case study"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "very deep convolutional networks for large scale image recognition"

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.CV(Computer Vision and Pattern Recognition)