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: "energy efficient convnets through approximate computing"
  Abstract: Recently convolutional neural networks (ConvNets) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection. However, ConvNet algorithms are typically very computation and memory intensive. In order to be able to embed ConvNet-based classification into wearable platforms and embedded systems such as smartphones or ubiquitous electronics for the internet-of-things, their energy consumption should be reduced drastically. This paper proposes methods based on approximate computing to reduce energy consumption in state-of-the-art ConvNet accelerators. By combining techniques both at the system- and circuit level, we can gain energy in the systems arithmetic: up to 30 X without losing classification accuracy and more than 100 X at 99% classification accuracy, compared to the commonly used 16-bit fixed point number format.

Connected through hyperedge HE-1 (induced by source paper "an application specific vliw processor with vector instruction set for cnn acceleration"):
  with members:
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "origami a 803 gop s w convolutional network accelerator"
    - "fixed point quantization of deep convolutional networks"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "magnetoresistive ram for error resilient xnor nets"):
  with members:
    - "deep learning with limited numerical precision"
    - "going deeper with convolutions"
    - "network in network"

Connected through hyperedge HE-3 (induced by source paper "energy based tuning of convolutional neural networks on multi gpus"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "benchmarking state of the art deep learning software tools"
    - "ssd single shot multibox detector"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "designing energy efficient convolutional neural networks using energy aware pruning"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "minimum energy quantized neural networks"):
  with members:
    - "large scale evolution of image classifiers"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "trained ternary quantization"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "yodann an architecture for ultra low power binary weight cnn acceleration"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "high performance binarized neural networks trained on the imagenet classification task"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural networks"
    - "dialog based language learning"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "yodann an ultra low power convolutional neural network accelerator based on binary weights"):
  with members:
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "neural networks with few multiplications"

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)