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: "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
  Abstract: We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights. The set constraint that characterizes the quantization of weights is not imposed until the late stage of training, and a sequence of pseudo quantized weights is maintained. Specifically, we relax the hard constraint into a continuous regularizer via Moreau envelope, which turns out to be the squared Euclidean distance to the set of quantized weights. The pseudo quantized weights are obtained by linearly interpolating between the float weights and their quantizations. A continuation strategy is adopted to push the weights towards the quantized state by gradually increasing the regularization parameter. In the second phase, exact quantization scheme with a small learning rate is invoked to guarantee fully quantized weights. We test BinaryRelax on the benchmark CIFAR-10 and CIFAR-100 color image datasets to demonstrate the superiority of the relaxed quantization approach and the improved accuracy over the state-of-the-art training methods. Finally, we prove the convergence of BinaryRelax under an approximate orthogonality condition.

Connected through hyperedge HE-1 (induced by source paper "gradient ell_1 regularization for quantization robustness"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "defensive quantization when efficiency meets robustness"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "sparse dnns with improved adversarial robustness"
    - "the singular values of convolutional layers"

Connected through hyperedge HE-2 (induced by source paper "understanding straight through estimator in training activation quantized neural nets"):
  with members:
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "deep neural nets with interpolating function as output activation"
    - "learning one convolutional layer with overlapping patches"
    - "loss aware weight quantization of deep networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

Connected through hyperedge HE-3 (induced by source paper "mirror descent view for neural network quantization"):
  with members:
    - "quantization networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxquant quantized neural networks via proximal operators"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "a survey on methods and theories of quantized neural networks"
    - "relaxed quantization for discretized neural networks"
    - "snip single shot network pruning based on connection sensitivity"

Connected through hyperedge HE-4 (induced by source paper "discovering low precision networks close to full precision networks for efficient embedded inference"):
  with members:
    - "uniq uniform noise injection for the quantization of neural networks"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "high accuracy low precision training"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "pact parameterized clipping activation for quantized neural networks"
    - "averaging weights leads to wider optima and better generalization"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-5 (induced by source paper "dac data free automatic acceleration of convolutional networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "rehar robust and efficient human activity recognition"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "blended coarse gradient descent for full quantization of deep neural networks"):
  with members:
    - "deep neural nets with interpolating function as output activation"
    - "pact parameterized clipping activation for quantized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "training quantized nets a deeper understanding"
    - "deep learning with low precision by half wave gaussian quantization"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-7 (induced by source paper "proxquant quantized neural networks via proximal operators"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "loss aware weight quantization of deep networks"
    - "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-8 (induced by source paper "proximal mean field for neural network quantization"):
  with members:
    - "proxquant quantized neural networks via proximal operators"
    - "a survey on methods and theories of quantized neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "wrpn wide reduced precision networks"
    - "snapshot ensembles train 1 get m for free"
    - "efficient linear programming for dense crfs"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "loss aware binarization of deep 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.CV(Computer Vision and Pattern Recognition)