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: "per tensor fixed point quantization of the back propagation algorithm"
  Abstract: The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems. Many network complexity reduction techniques have been proposed including fixed-point implementation. However, a systematic approach for designing full fixed-point training and inference of deep neural networks remains elusive. We describe a precision assignment methodology for neural network training in which all network parameters, i.e., activations and weights in the feedforward path, gradients and weight accumulators in the feedback path, are assigned close to minimal precision. The precision assignment is derived analytically and enables tracking the convergence behavior of the full precision training, known to converge a priori. Thus, our work leads to a systematic methodology of determining suitable precision for fixed-point training. The near optimality (minimality) of the resulting precision assignment is validated empirically for four networks on the CIFAR-10, CIFAR-100, and SVHN datasets. The complexity reduction arising from our approach is compared with other fixed-point neural network designs.

Connected through hyperedge HE-1 (induced by source paper "sparse weight activation training"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "swalp stochastic weight averaging in low precision training"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "towards unified int8 training for convolutional neural network"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "learned step size quantization"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-3 (induced by source paper "quantized reinforcement learning quarl"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "defensive quantization when efficiency meets robustness"
    - "model compression via distillation and quantization"
    - "sbnet sparse blocks network for fast inference"
    - "pruned and structurally sparse neural networks"
    - "relaxed quantization for discretized neural networks"
    - "learning to drive in a day"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "constructing energy efficient mixed precision neural networks through principal component analysis for edge intelligence"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "a low effort approach to structured cnn design using pca"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn training and inference using wide reduced precision networks"

Connected through hyperedge HE-5 (induced by source paper "training high performance and large scale deep neural networks with full 8 bit integers"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "training and inference with integers in deep neural networks"
    - "learning low precision deep neural networks through regularization"
    - "training deep neural networks with 8 bit floating point numbers"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mixed precision training"

Connected through hyperedge HE-6 (induced by source paper "pca driven hybrid network design for enabling intelligence at the edge"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "a low effort approach to structured cnn design using pca"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"
    - "low precision batch normalized activations"
    - "wrpn wide reduced precision 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)