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: "hello edge keyword spotting on microcontrollers"
  Abstract: Keyword spotting (KWS) is a critical component for enabling speech based user interactions on smart devices. It requires real-time response and high accuracy for good user experience. Recently, neural networks have become an attractive choice for KWS architecture because of their superior accuracy compared to traditional speech processing algorithms. Due to its always-on nature, KWS application has highly constrained power budget and typically runs on tiny microcontrollers with limited memory and compute capability. The design of neural network architecture for KWS must consider these constraints. In this work, we perform neural network architecture evaluation and exploration for running KWS on resource-constrained microcontrollers. We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements. We show that it is possible to optimize these neural network architectures to fit within the memory and compute constraints of microcontrollers without sacrificing accuracy. We further explore the depthwise separable convolutional neural network (DS-CNN) and compare it against other neural network architectures. DS-CNN achieves an accuracy of 95.4%, which is ~10% higher than the DNN model with similar number of parameters.

Connected through hyperedge HE-1 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-2 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "improving efficiency in neural network accelerator using operands hamming distance optimization"
    - "bit efficient quantization for deep neural networks"
    - "cold case the lost mnist digits"
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"
    - "spike train level backpropagation for training deep recurrent spiking neural networks"
    - "energy aware neural architecture optimization with fast splitting steepest descent"

Connected through hyperedge HE-3 (induced by source paper "performance oriented neural architecture search"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quenn quantization engine for low power neural networks"
    - "streaming end to end speech recognition for mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "optimal dnn primitive selection with partitioned boolean quadratic programming"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "smash one shot model architecture search through hypernetworks"

Connected through hyperedge HE-4 (induced by source paper "improving efficiency in neural network accelerator using operands hamming distance optimization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "mnasnet platform aware neural architecture search for mobile"
    - "in datacenter performance analysis of a tensor processing unit"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (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"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"

Connected through hyperedge HE-6 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-7 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-8 (induced by source paper "fq conv fully quantized convolution for efficient and accurate inference"):
  with members:
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "deep neural network compression with single and multiple level quantization"
    - "label refinery improving imagenet classification through label progression"
    - "deep residual learning for small footprint keyword spotting"
    - "the high dimensional geometry of binary neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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.SD(Sound)