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: "overcoming challenges in fixed point training of deep convolutional networks"
  Abstract: It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging. The stochastic gradient descent algorithm becomes unstable in the presence of noisy gradient updates resulting from arithmetic with limited numeric precision. One of the well-accepted solutions facilitating the training of low precision fixed point networks is stochastic rounding. However, to the best of our knowledge, the source of the instability in training neural networks with noisy gradient updates has not been well investigated. This work is an attempt to draw a theoretical connection between low numerical precision and training algorithm stability. In doing so, we will also propose and verify through experiments methods that are able to improve the training performance of deep convolutional networks in fixed point.

Connected through hyperedge HE-1 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "matrix and tensor decompositions for training binary neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"

Connected through hyperedge HE-2 (induced by source paper "ifq net integrated fixed point quantization networks for embedded vision"):
  with members:
    - "deep learning with low precision by half wave gaussian quantization"
    - "automated flow for compressing convolution neural networks for efficient edge computation with fpga"
    - "mask r cnn"
    - "hardware oriented approximation of convolutional neural networks"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "fixed point quantization of deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "a computing kernel for network binarization on pytorch"):
  with members:
    - "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"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "a survey on methods and theories of quantized neural networks"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "minimum energy quantized neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning with low precision by half wave gaussian quantization"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "quantized memory augmented neural networks"):
  with members:
    - "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"
    - "recurrent neural networks with limited numerical precision"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "deep learning with limited numerical precision"
    - "neural turing machines"

Connected through hyperedge HE-7 (induced by source paper "fixed point quantization of deep convolutional networks"):
  with members:
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "compressing neural networks with the hashing trick"
    - "deep learning with limited numerical precision"
    - "hyper parameter optimization of deep convolutional networks for object recognition"

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)