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: "a systematic approach to blocking convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations. Most implementations heuristically block the computation to deal with the large data sizes and high data reuse of CNNs. This paper explores how to block CNN computations for memory locality by creating an analytical model for CNN-like loop nests. Using this model we automatically derive optimized blockings for common networks that improve the energy efficiency of custom hardware implementations by up to an order of magnitude. Compared to traditional CNN CPU implementations based on highly-tuned, hand-optimized BLAS libraries,our x86 programs implementing the optimal blocking reduce the number of memory accesses by up to 90%.

Connected through hyperedge HE-1 (induced by source paper "marvel a decoupled model driven approach for efficiently mapping convolutions on spatial dnn accelerators"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "ecnn a block based and highly parallel cnn accelerator for edge inference"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "dslr quality photos on mobile devices with deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "supporting very large models using automatic dataflow graph partitioning"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "profile guided memory optimization for deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "device placement optimization with reinforcement learning"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "wide residual networks"

Connected through hyperedge HE-4 (induced by source paper "asv accelerated stereo vision system"):
  with members:
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "dnn dataflow choice is overrated"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"

Connected through hyperedge HE-5 (induced by source paper "optimally scheduling cnn convolutions for efficient memory access"):
  with members:
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "ristretto hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "communication lower bound in convolution accelerators"):
  with members:
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "optimizing memory efficiency for convolution kernels on kepler gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep learning with limited numerical precision"
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fast training of convolutional networks through ffts"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "restructuring batch normalization to accelerate cnn training"):
  with members:
    - "partitioning compute units in cnn acceleration for statistical memory traffic shaping"
    - "understanding batch normalization"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "dnn dataflow choice is overrated"):
  with members:
    - "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"
    - "programming heterogeneous systems from an image processing dsl"
    - "recurrent highway networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)