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: "on the universal approximability and complexity bounds of quantized relu neural networks"
  Abstract: Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power of quantized neural networks. First, we prove the universal approximability of quantized ReLU networks on a wide class of functions. Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures. Our results reveal that, to attain an approximation error bound of $\epsilon$, the number of weights needed by a quantized network is no more than $\mathcal{O}\left(\log^5(1/\epsilon)\right)$ times that of an unquantized network. This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques. To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.

Connected through hyperedge HE-1 (induced by source paper "dlbc a deep learning based consensus in blockchains for deep learning services"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "exploiting computation power of blockchain for biomedical image segmentation"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"
    - "proof of search combining blockchain consensus formation with solving optimization problems"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "privacy aware cost effective scheduling considering non schedulable appliances in smart home"
    - "real time adversarial attacks"
    - "measuring human perception to improve handwritten document transcription"

Connected through hyperedge HE-2 (induced by source paper "achieving super linear speedup across multi fpga for real time dnn inference"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "quantization of fully convolutional networks for accurate biomedical image segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "recent trends in deep learning based natural language processing"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "real time boiler control optimization with machine learning"):
  with members:
    - "pbgen partial binarization of deconvolution based generators for edge intelligence"
    - "quantization of fully convolutional networks for accurate biomedical image segmentation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "pbgen partial binarization of deconvolution based generators for edge intelligence"):
  with members:
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "pca driven hybrid network design for enabling intelligence at the edge"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "binary generative adversarial networks for image retrieval"

Connected through hyperedge HE-5 (induced by source paper "universal approximation with quadratic deep networks"):
  with members:
    - "quadratic autoencoder q ae for low dose ct denoising"
    - "efficient sparse winograd convolutional neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "fuzzy logic interpretation of artificial neural networks"
    - "dense associative memory is robust to adversarial inputs"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "a new type of neurons for machine learning"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-6 (induced by source paper "proxquant quantized neural networks via proximal operators"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-7 (induced by source paper "the universal approximation power of finite width deep relu networks"):
  with members:
    - "optimal approximation with sparsely connected deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "why deep neural networks for function approximation"
    - "representation benefits of deep feedforward 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)