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: "sparse sparse architecture search for cnns on resource constrained microcontrollers"
  Abstract: The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from automobiles to medical devices and office equipment. The Internet of Things (IoT) promises to inject machine learning into many of these every-day objects via tiny, cheap MCUs. However, these resource-impoverished hardware platforms severely limit the complexity of machine learning models that can be deployed. For example, although convolutional neural networks (CNNs) achieve state-of-the-art results on many visual recognition tasks, CNN inference on MCUs is challenging due to severe finite memory limitations. To circumvent the memory challenge associated with CNNs, various alternatives have been proposed that do fit within the memory budget of an MCU, albeit at the cost of prediction accuracy. This paper challenges the idea that CNNs are not suitable for deployment on MCUs. We demonstrate that it is possible to automatically design CNNs which generalize well, while also being small enough to fit onto memory-limited MCUs. Our Sparse Architecture Search method combines neural architecture search with pruning in a single, unified approach, which learns superior models on four popular IoT datasets. The CNNs we find are more accurate and up to $4.35\times$ smaller than previous approaches, while meeting the strict MCU working memory constraint.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "winograd convolution for dnns beyond linear polynomials"
    - "visual wake words dataset"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "searching for mobilenetv3"
    - "efficient winograd convolution via integer arithmetic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "neural networks on microcontrollers saving memory at inference via operator reordering"

Connected through hyperedge HE-2 (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"
    - "mlperf inference benchmark"
    - "haq hardware aware automated quantization with mixed precision"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "hello edge keyword spotting on microcontrollers"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"

Connected through hyperedge HE-3 (induced by source paper "visual wake words dataset"):
  with members:
    - "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"
    - "not all ops are created equal"

Connected through hyperedge HE-4 (induced by source paper "neural networks on microcontrollers saving memory at inference via operator reordering"):
  with members:
    - "visual wake words dataset"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "accelerating convolutional neural networks via activation map compression"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "training dnn iot applications for deployment on analog nvm crossbars"):
  with members:
    - "differentiable quantization of deep neural networks"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "haq hardware aware automated quantization with mixed precision"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"

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)