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: "eyeriss v2 a flexible and high performance accelerator for emerging deep neural networks"
  Abstract: The design of DNNs has increasingly focused on reducing the computational complexity in addition to improving accuracy. While emerging DNNs tend to have fewer weights and operations, they also reduce the amount of data reuse with more widely varying layer shapes and sizes. This leads to a diverse set of DNNs, ranging from large ones with high reuse (e.g., AlexNet) to compact ones with high bandwidth requirements (e.g., MobileNet). However, many existing DNN processors depend on certain DNN properties, e.g., a large number of channels, to achieve high performance and energy efficiency and do not have sufficient flexibility to efficiently process a diverse set of DNNs. In this work, we present Eyexam, a performance analysis framework that quantitatively identifies the sources of performance loss in DNN processors. It highlights two architectural bottlenecks in many existing designs. First, their dataflows are not flexible enough to adapt to the varying layer shapes and sizes of different DNNs. Second, their network-on-chip (NoC) can't adapt to support both high data reuse and high bandwidth scenarios. Based on this analysis, we present Eyeriss v2, a high-performance DNN accelerator that adapts to a wide range of DNNs. Eyeriss v2 has a new dataflow, called Row-Stationary Plus (RS+), that enables the spatial tiling of data from all dimensions to fully utilize the parallelism for high performance. To support RS+, it has a low-cost and scalable NoC design, called hierarchical mesh, that connects the high-bandwidth global buffer to the array of processing elements (PEs) in a two-level hierarchy. This enables high-bandwidth data delivery while still being able to harness any available data reuse. Compared with Eyeriss, Eyeriss v2 has a performance increase of 10.4x-17.9x for 256 PEs, 37.7x-71.5x for 1024 PEs, and 448.8x-1086.7x for 16384 PEs on DNNs with widely varying amounts of data reuse.

Connected through hyperedge HE-1 (induced by source paper "a flexible fpga accelerator for convolutional neural networks"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-2 (induced by source paper "nesta hamming weight compression based neural proc engine"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "neural heterogeneous scheduler"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "deep reinforcement learning for multi resource multi machine job scheduling"

Connected through hyperedge HE-4 (induced by source paper "ratatoskr an open source framework for in depth power performance and area analysis in 3d nocs"):
  with members:
    - "nocs in heterogeneous 3d socs co design of routing strategies and microarchitectures"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "limits on fundamental limits to computation"

Connected through hyperedge HE-5 (induced by source paper "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "aggregated residual transformations for deep neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "u net convolutional networks for biomedical image segmentation"

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)