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: "rethinking floating point for deep learning"
  Abstract: Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relative to this baseline; it remains energy inefficient, and word size reduction yields drastic loss in needed dynamic range. We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson's posit format. With no network retraining, and drop-in replacement of all math and float32 parameters via round-to-nearest-even only, this open-sourced 8-bit log float is within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN model on ImageNet. Unlike int8 quantization, it is still a general purpose floating point arithmetic, interpretable out-of-the-box. Our 8/38-bit log float multiply-add is synthesized and power profiled at 28 nm at 0.96x the power and 1.12x the area of 8/32-bit integer multiply-add. In 16 bits, our log float multiply-add is 0.59x the power and 0.68x the area of IEEE 754 float16 fused multiply-add, maintaining the same signficand precision and dynamic range, proving useful for training ASICs as well.

Connected through hyperedge HE-1 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "nvidia tensor core programmability performance precision"
    - "training and inference with integers in deep neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-2 (induced by source paper "stochastic rounding and reduced precision fixed point arithmetic for solving neural ordinary differential equations"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed precision training"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "rounding methods for neural networks with low resolution synaptic weights"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-3 (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "adaptive neural networks for efficient inference"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-4 (induced by source paper "cheetah mixed low precision hardware software co design framework for dnns on the edge"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-5 (induced by source paper "deep learning training on the edge with low precision posits"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "template based posit multiplication for training and inferring in neural networks"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-6 (induced by source paper "performance efficiency trade off of low precision numerical formats in deep neural networks"):
  with members:
    - "deep positron a deep neural network using the posit number system"
    - "training and inference with integers in deep neural networks"
    - "deep learning inference on embedded devices fixed point vs posit"
    - "wrpn apprentice methods for training and inference using low precision numerics"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "ristretto hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "auto precision scaling for distributed deep learning"):
  with members:
    - "image classification at supercomputer scale"
    - "training deep neural networks with 8 bit floating point numbers"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "virtualizing deep neural networks for memory efficient neural network design"
    - "training deep nets with sublinear memory cost"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-8 (induced by source paper "training deep neural networks using posit number system"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "training and inference with integers in deep neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed precision training"
    - "convolutional neural networks using logarithmic data representation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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.NA(Numerical Analysis)