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: "cmsis nn efficient neural network kernels for arm cortex m cpus"
  Abstract: Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication. This paper presents CMSIS-NN, efficient kernels developed to maximize the performance and minimize the memory footprint of neural network (NN) applications on Arm Cortex-M processors targeted for intelligent IoT edge devices. Neural network inference based on CMSIS-NN kernels achieves 4.6X improvement in runtime/throughput and 4.9X improvement in energy efficiency.

Connected through hyperedge HE-1 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference"):
  with members:
    - "visual wake words dataset"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "lffd a light and fast face detector for edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "haq hardware aware automated quantization with mixed precision"
    - "neura ghe exploiting cpu fpga synergies for efficient and flexible cnn inference acceleration on zynq socs"
    - "pact parameterized clipping activation for quantized neural networks"
    - "minimum energy quantized neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-3 (induced by source paper "benchmarking tinyml systems challenges and direction"):
  with members:
    - "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "mlperf inference benchmark"
    - "haq hardware aware automated quantization with mixed precision"
    - "hello edge keyword spotting on microcontrollers"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"

Connected through hyperedge HE-4 (induced by source paper "a c code generator for fast inference and simple deployment of convolutional neural networks on resource constrained systems"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "glow graph lowering compiler techniques for neural networks"
    - "yolo9000 better faster stronger"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "visual wake words dataset"):
  with members:
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "searching for mobilenetv3"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "rethinking machine learning development and deployment for edge devices"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "speech commands a dataset for limited vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"):
  with members:
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "low bit quantization of neural networks for efficient inference"
    - "learning low precision neural networks without straight through estimator ste"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "ifq net integrated fixed point quantization networks for embedded vision"
    - "learned step size quantization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "an optimized recurrent unit for ultra low power keyword spotting"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "improving speech recognition by revising gated recurrent units"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "minimal gated unit for recurrent neural networks"
    - "recurrent neural networks with limited numerical precision"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-8 (induced by source paper "tuple oriented compression for large scale mini batch stochastic gradient descent"):
  with members:
    - "a cost based optimizer for gradient descent optimization"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "towards linear algebra over normalized data"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "compressing neural networks with the hashing trick"

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.NE(Neural and Evolutionary Computing)