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 "ristretto hardware oriented approximation of convolutional neural networks"):
  Abstract: Convolutional neural networks (CNN) have achieved major breakthroughs in recent years. Their performance in computer vision have matched and in some areas even surpassed human capabilities. Deep neural networks can capture complex non-linear features; however this ability comes at the cost of high computational and memory requirements. State-of-art networks require billions of arithmetic operations and millions of parameters. To enable embedded devices such as smartphones, Google glasses and monitoring cameras with the astonishing power of deep learning, dedicated hardware accelerators can be used to decrease both execution time and power consumption. In applications where fast connection to the cloud is not guaranteed or where privacy is important, computation needs to be done locally. Many hardware accelerators for deep neural networks have been proposed recently. A first important step of accelerator design is hardware-oriented approximation of deep networks, which enables energy-efficient inference. We present Ristretto, a fast and automated framework for CNN approximation. Ristretto simulates the hardware arithmetic of a custom hardware accelerator. The framework reduces the bit-width of network parameters and outputs of resource-intense layers, which reduces the chip area for multiplication units significantly. Alternatively, Ristretto can remove the need for multipliers altogether, resulting in an adder-only arithmetic. The tool fine-tunes trimmed networks to achieve high classification accuracy. Since training of deep neural networks can be time-consuming, Ristretto uses highly optimized routines which run on the GPU. This enables fast compression of any given network. Given a maximum tolerance of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.

Includes members:
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "hardware oriented approximation of convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "light multi segment activation for model compression")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "xnor net imagenet classification using binary convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "resiliency of deep neural networks under quantization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "post training quantization with multiple points mixed precision without mixed precision")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "fixed point quantization of deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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)