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: "deep positron a deep neural network using the posit number system"
  Abstract: The recent surge of interest in Deep Neural Networks (DNNs) has led to increasingly complex networks that tax computational and memory resources. Many DNNs presently use 16-bit or 32-bit floating point operations. Significant performance and power gains can be obtained when DNN accelerators support low-precision numerical formats. Despite considerable research, there is still a knowledge gap on how low-precision operations can be realized for both DNN training and inference. In this work, we propose a DNN architecture, Deep Positron, with posit numerical format operating successfully at $\leq$8 bits for inference. We propose a precision-adaptable FPGA soft core for exact multiply-and-accumulate for uniform comparison across three numerical formats, fixed, floating-point and posit. Preliminary results demonstrate that 8-bit posit has better accuracy than 8-bit fixed or floating-point for three different low-dimensional datasets. Moreover, the accuracy is comparable to 32-bit floating-point on a Xilinx Virtex-7 FPGA device. The trade-offs between DNN performance and hardware resources, i.e. latency, power, and resource utilization, show that posit outperforms in accuracy and latency at 8-bit and below.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "winograd convolution for dnns beyond linear polynomials"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "searching for mobilenetv3"
    - "efficient winograd convolution via integer arithmetic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-2 (induced by source paper "adaptivfloat a floating point based data type for resilient deep learning inference"):
  with members:
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "effective quantization approaches for recurrent neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "wrpn wide reduced precision networks"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (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"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-4 (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"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-5 (induced by source paper "performance efficiency trade off of low precision numerical formats in deep neural networks"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "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-6 (induced by source paper "training deep neural networks using posit number system"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "template based posit multiplication for training and inferring in neural networks"):
  with members:
    - "performance efficiency trade off of low precision numerical formats in deep neural 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.DC(Distributed, Parallel, and Cluster Computing)