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: "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
  Abstract: Deep convolutional neural networks (CNN) have shown their good performances in many computer vision tasks. However, the high computational complexity of CNN involves a huge amount of data movements between the computational processor core and memory hierarchy which occupies the major of the power consumption. This paper presents Chain-NN, a novel energy-efficient 1D chain architecture for accelerating deep CNNs. Chain-NN consists of the dedicated dual-channel process engines (PE). In Chain-NN, convolutions are done by the 1D systolic primitives composed of a group of adjacent PEs. These systolic primitives, together with the proposed column-wise scan input pattern, can fully reuse input operand to reduce the memory bandwidth requirement for energy saving. Moreover, the 1D chain architecture allows the systolic primitives to be easily reconfigured according to specific CNN parameters with fewer design complexity. The synthesis and layout of Chain-NN is under TSMC 28nm process. It costs 3751k logic gates and 352KB on-chip memory. The results show a 576-PE Chain-NN can be scaled up to 700MHz. This achieves a peak throughput of 806.4GOPS with 567.5mW and is able to accelerate the five convolutional layers in AlexNet at a frame rate of 326.2fps. 1421.0GOPS/W power efficiency is at least 2.5 to 4.1x times better than the state-of-the-art works.

Connected through hyperedge HE-1 (induced by source paper "full stack optimization for accelerating cnns with fpga validation"):
  with members:
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "pact parameterized clipping activation for quantized neural networks"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-2 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image classification at supercomputer scale"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "progressive neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "communication lower bound in convolution accelerators"):
  with members:
    - "optimizing memory efficiency for convolution kernels on kepler gpus"
    - "a systematic approach to blocking convolutional neural networks"
    - "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-4 (induced by source paper "recent advances in convolutional neural network acceleration"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "mec memory efficient convolution for deep neural network"
    - "coordinating filters for faster deep neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-5 (induced by source paper "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"):
  with members:
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-6 (induced by source paper "multi mode inference engine for convolutional neural networks"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "learning structured sparsity in deep neural networks"
    - "a 0 3 2 6 tops w precision scalable processor for real time large scale convnets"
    - "eie efficient inference engine on compressed deep neural network"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "matic learning around errors for efficient low voltage neural network accelerators"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep learning with limited numerical precision"
    - "caffe convolutional architecture for fast feature embedding"

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.AR(Hardware Architecture)