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: "wrpn apprentice methods for training and inference using low precision numerics"
  Abstract: Today's high performance deep learning architectures involve large models with numerous parameters. Low precision numerics has emerged as a popular technique to reduce both the compute and memory requirements of these large models. However, lowering precision often leads to accuracy degradation. We describe three schemes whereby one can both train and do efficient inference using low precision numerics without hurting accuracy. Finally, we describe an efficient hardware accelerator that can take advantage of the proposed low precision numerics.

Connected through hyperedge HE-1 (induced by source paper "performance efficiency trade off of low precision numerical formats in deep neural networks"):
  with members:
    - "deep positron a deep neural network using the posit number system"
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "deep learning inference on embedded devices fixed point vs posit"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "ristretto hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "generalized ternary connect end to end learning and compression of multiplication free deep neural networks"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "bitnet bit regularized deep neural networks"
    - "convolutional neural networks using logarithmic data representation"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "deep positron a deep neural network using the posit number system"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "deep learning inference on embedded devices fixed point vs posit"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "low precision arithmetic for deep learning"

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)