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: "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
  Abstract: The data partitioning and scheduling strategies used by DNN accelerators to leverage reuse and perform staging are known as dataflow, and they directly impact the performance and energy efficiency of DNN accelerator designs. An accelerator microarchitecture dictates the dataflow(s) that can be employed to execute a layer or network. Selecting an optimal dataflow for a layer shape can have a large impact on utilization and energy efficiency, but there is a lack of understanding on the choices and consequences of dataflows, and of tools and methodologies to help architects explore the co-optimization design space. In this work, we first introduce a set of data-centric directives to concisely specify the space of DNN dataflows in a compilerfriendly form. We then show how these directives can be analyzed to infer various forms of reuse and to exploit them using hardware capabilities. We codify this analysis into an analytical cost model, MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Reuse and Occupancy), that estimates various cost-benefit tradeoffs of a dataflow including execution time and energy efficiency for a DNN model and hardware configuration. We demonstrate the use of MAESTRO to drive a hardware design space exploration (DSE) experiment, which searches across 480M designs to identify 2.5M valid designs at an average rate of 0.17M designs per second, including Pareto-optimal throughput- and energy-optimized design points.

Connected through hyperedge HE-1 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics"):
  with members:
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "face recognition with hybrid efficient convolution algorithms on fpgas"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-2 (induced by source paper "deep learning without weight transport"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "unsupervised learning by competing hidden units"
    - "direct feedback alignment with sparse connections for local learning"
    - "loss landscapes of regularized linear autoencoders"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "herald optimizing heterogeneous dnn accelerators for edge devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ai benchmark running deep neural networks on android smartphones"
    - "learning depth from single images with deep neural network embedding focal length"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "aggregated residual transformations for deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "direct feedback alignment with sparse connections for local learning"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "deep supervised learning using local errors"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "difference target propagation"

Connected through hyperedge HE-5 (induced by source paper "understanding the impact of on chip communication on dnn accelerator performance"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep residual learning for image recognition"
    - "deeppose human pose estimation via 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)