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: "rebnet residual binarized neural network"
  Abstract: This paper proposes ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA. Binary neural networks offer an intriguing opportunity for deploying large-scale deep learning models on resource-constrained devices. Binarization reduces the memory footprint and replaces the power-hungry matrix-multiplication with light-weight XnorPopcount operations. However, binary networks suffer from a degraded accuracy compared to their fixed-point counterparts. We show that the state-of-the-art methods for optimizing binary networks accuracy, significantly increase the implementation cost and complexity. To compensate for the degraded accuracy while adhering to the simplicity of binary networks, we devise the first reconfigurable scheme that can adjust the classification accuracy based on the application. Our proposition improves the classification accuracy by representing features with multiple levels of residual binarization. Unlike previous methods, our approach does not exacerbate the area cost of the hardware accelerator. Instead, it provides a tradeoff between throughput and accuracy while the area overhead of multi-level binarization is negligible.

Connected through hyperedge HE-1 (induced by source paper "least squares binary quantization of neural networks"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (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"
    - "a hardware friendly algorithm for scalable training and deployment of dimensionality reduction models on fpga"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "lutnet learning fpga configurations for highly efficient neural network inference"):
  with members:
    - "lutnet rethinking inference in fpga soft logic"
    - "focused quantization for sparse cnns"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "in datacenter performance analysis of a tensor processing unit"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "finn a framework for fast scalable binarized neural network inference"

Connected through hyperedge HE-4 (induced by source paper "xonn xnor based oblivious deep neural network inference"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "scalable and accurate deep learning for electronic health records"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "towards accurate binary convolutional neural network"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-5 (induced by source paper "ascai adaptive sampling for acquiring compact ai"):
  with members:
    - "codex bit flexible encoding for streaming based fpga acceleration of dnns"
    - "amc automl for model compression and acceleration on mobile devices"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "a novel channel pruning method for deep neural network compression"
    - "data driven sparse structure selection for deep neural networks"
    - "large scale evolution of image classifiers"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-6 (induced by source paper "lutnet rethinking inference in fpga soft logic"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "in datacenter performance analysis of a tensor processing unit"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "finn a framework for fast scalable binarized neural network inference"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"

Connected through hyperedge HE-7 (induced by source paper "rethinking arithmetic for deep neural networks"):
  with members:
    - "lutnet rethinking inference in fpga soft logic"
    - "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "nullanet training deep neural networks for reduced memory access inference"):
  with members:
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "deploying customized data representation and approximate computing in machine learning applications"
    - "finn a framework for fast scalable binarized neural network inference"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight 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)