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: "performance efficiency trade off of low precision numerical formats in deep neural networks"
  Abstract: Deep neural networks (DNNs) have been demonstrated as effective prognostic models across various domains, e.g. natural language processing, computer vision, and genomics. However, modern-day DNNs demand high compute and memory storage for executing any reasonably complex task. To optimize the inference time and alleviate the power consumption of these networks, DNN accelerators with low-precision representations of data and DNN parameters are being actively studied. An interesting research question is in how low-precision networks can be ported to edge-devices with similar performance as high-precision networks. In this work, we employ the fixed-point, floating point, and posit numerical formats at ≤8-bit precision within a DNN accelerator, Deep Positron, with exact multiply-and-accumulate (EMAC) units for inference. A unified analysis quantifies the trade-offs between overall network efficiency and performance across five classification tasks. Our results indicate that posits are a natural fit for DNN inference, outperforming at ≤8-bit precision, and can be realized with competitive resource requirements relative to those of floating point.

Connected through hyperedge HE-1 (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"
    - "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"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-3 (induced by source paper "template based posit multiplication for training and inferring in neural networks"):
  with members:
    - "deep positron a deep neural network using the posit number system"

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)