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: "bitwise neural networks"
  Abstract: Based on the assumption that there exists a neural network that efficiently represents a set of Boolean functions between all binary inputs and outputs, we propose a process for developing and deploying neural networks whose weight parameters, bias terms, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the feedforward pass. The proposed Bitwise Neural Network (BNN) is especially suitable for resource-constrained environments, since it replaces either floating or fixed-point arithmetic with significantly more efficient bitwise operations. Hence, the BNN requires for less spatial complexity, less memory bandwidth, and less power consumption in hardware. In order to design such networks, we propose to add a few training schemes, such as weight compression and noisy backpropagation, which result in a bitwise network that performs almost as well as its corresponding real-valued network. We test the proposed network on the MNIST dataset, represented using binary features, and show that BNNs result in competitive performance while offering dramatic computational savings.

Connected through hyperedge HE-1 (induced by source paper "autoq automated kernel wise neural network quantization"):
  with members:
    - "optimizing bit serial matrix multiplication for reconfigurable computing"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-2 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "training and inference with integers in deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-3 (induced by source paper "fpga based accelerators of deep learning networks for learning and classification a review"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "deep learning for sentiment analysis a survey"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
    - "hardware for machine learning challenges and opportunities"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "parallel multi channel convolution using general matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "quotient two party secure neural network training and prediction"):
  with members:
    - "mobile sensor data anonymization"
    - "on the convergence of adam and beyond"
    - "xonn xnor based oblivious deep neural network inference"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "understanding batch normalization"
    - "tapas tricks to accelerate encrypted prediction as a service"

Connected through hyperedge HE-5 (induced by source paper "autoqb automl for network quantization and binarization on mobile devices"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "alternating multi bit quantization for recurrent neural networks"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "network sketching exploiting binary structure in deep cnns"
    - "learning transferable architectures for scalable image recognition"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "sampling free learning of bayesian quantized neural networks"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "lightweight probabilistic deep networks"
    - "probabilistic binary neural networks"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a neural network prefetcher for arbitrary memory access patterns"):
  with members:
    - "learning memory access patterns"
    - "in datacenter performance analysis of a tensor processing unit"
    - "lstm a search space odyssey"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "neural random access machines"

Connected through hyperedge HE-8 (induced by source paper "quantization loss re learning method"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "bidirectional lstm crf models for sequence tagging"
    - "training deep neural networks with low precision 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.LG(Machine Learning)