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: "a hardware friendly algorithm for scalable training and deployment of dimensionality reduction models on fpga"
  Abstract: With ever-increasing application of machine learning models in various domains such as image classification, speech recognition and synthesis, and health care, designing efficient hardware for these models has gained a lot of popularity. While the majority of researches in this area focus on efficient deployment of machine learning models (a.k.a inference), this work concentrates on challenges of training these models in hardware. In particular, this paper presents a high-performance, scalable, reconfigurable solution for both training and deployment of different dimensionality reduction models in hardware by introducing a hardware-friendly algorithm. Compared to state-of-the-art implementations, our proposed algorithm and its hardware realization decrease resource consumption by 50\% without any degradation in accuracy.

Connected through hyperedge HE-1 (induced by source paper "codex bit flexible encoding for streaming based fpga acceleration of dnns"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "wrpn wide reduced precision networks"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "rebnet residual binarized neural network"

Connected through hyperedge HE-2 (induced by source paper "deploying customized data representation and approximate computing in machine learning applications"):
  with members:
    - "fft based deep learning deployment in embedded systems"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "high performance fpga implementation of equivariant adaptive separation via independence algorithm for independent component analysis"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural networks"
    - "ternary weight networks"

Connected through hyperedge HE-3 (induced by source paper "rebnet residual binarized neural network"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning with low precision by half wave gaussian quantization"
    - "ternary neural networks with fine grained quantization"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"

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.LG(Machine Learning)