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: "parallel multi channel convolution using general matrix multiplication"
  Abstract: Convolutional neural networks (CNNs) have emerged as one of the most successful machine learning technologies for image and video processing. The most computationally intensive parts of CNNs are the convolutional layers, which convolve multi-channel images with multiple kernels. A common approach to implementing convolutional layers is to expand the image into a column matrix (im2col) and perform Multiple Channel Multiple Kernel (MCMK) convolution using an existing parallel General Matrix Multiplication (GEMM) library. This im2col conversion greatly increases the memory footprint of the input matrix and reduces data locality. #R##N#In this paper we propose a new approach to MCMK convolution that is based on General Matrix Multiplication (GEMM), but not on im2col. Our algorithm eliminates the need for data replication on the input thereby enabling us to apply the convolution kernels on the input images directly. We have implemented several variants of our algorithm on a CPU processor and an embedded ARM processor. On the CPU, our algorithm is faster than im2col in most cases.

Connected through hyperedge HE-1 (induced by source paper "performance oriented neural architecture search"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quenn quantization engine for low power neural networks"
    - "streaming end to end speech recognition for mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "hello edge keyword spotting on microcontrollers"
    - "optimal dnn primitive selection with partitioned boolean quadratic programming"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "fpga based accelerators of deep learning networks for learning and classification a review"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "deep learning for sentiment analysis a survey"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
    - "hardware for machine learning challenges and opportunities"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

Connected through hyperedge HE-3 (induced by source paper "gated crf loss for weakly supervised semantic image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "towards closing the gap in weakly supervised semantic segmentation with dcnns combining local and global models"
    - "convolutional crfs for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-4 (induced by source paper "high performance deep learning via a single building block"):
  with members:
    - "stripe tensor compilation via the nested polyhedral model"
    - "large batch training for lstm and beyond"
    - "nvidia tensor core programmability performance precision"
    - "high accuracy low precision training"
    - "glow graph lowering compiler techniques for neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "coreset based neural network compression"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "incremental learning through deep adaptation"
    - "deformable convolutional networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "compression aware training of deep networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "n2n learning network to network compression via policy gradient reinforcement learning"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "anatomy of high performance deep learning convolutions on simd architectures"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication optimal convolutional neural nets"
    - "on scale out deep learning training for cloud and hpc"
    - "mixed precision training"
    - "low memory gemm based convolution algorithms for deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "clblast a tuned opencl blas library"):
  with members:
    - "input aware auto tuning of compute bound hpc kernels"
    - "sobol tensor trains for global sensitivity analysis"
    - "mixed precision training"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "cltune a generic auto tuner for opencl kernels"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-8 (induced by source paper "optimal dnn primitive selection with partitioned boolean quadratic programming"):
  with members:
    - "low memory gemm based convolution algorithms for deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "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.CV(Computer Vision and Pattern Recognition)