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: "comparative study of deep learning software frameworks"
  Abstract: Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of five deep learning frameworks, namely Caffe, Neon, TensorFlow, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important during the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algorithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best performance on GPU for training and deployment of LSTM networks. Caffe is the easiest for evaluating the performance of standard deep architectures. Finally, TensorFlow is a very flexible framework, similar to Theano, but its performance is currently not competitive compared to the other studied frameworks.

Connected through hyperedge HE-1 (induced by source paper "high performance i o for large scale deep learning"):
  with members:
    - "characterizing deep learning i o workloads in tensorflow"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-2 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications"):
  with members:
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "deepcache principled cache for mobile deep vision"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "a first look at deep learning apps on smartphones"
    - "serving deep learning models in a serverless platform"
    - "learning transferable architectures for scalable image recognition"
    - "tensorflow serving flexible high performance ml serving"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-3 (induced by source paper "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"):
  with members:
    - "moving deep learning into web browser how far can we go"
    - "deep learning towards mobile applications"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "a first look at deep learning apps on smartphones"
    - "lstm a search space odyssey"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-4 (induced by source paper "deep learning vs traditional computer vision"):
  with members:
    - "recurrent convolutional fusion for rgb d object recognition"
    - "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"
    - "hybrid scene compression for visual localization"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-5 (induced by source paper "a detailed comparative study of open source deep learning frameworks"):
  with members:
    - "benchmarking state of the art deep learning software tools"
    - "a tour of tensorflow"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "theano based large scale visual recognition with multiple gpus"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "efficient memory management for gpu based deep learning systems"):
  with members:
    - "training wide residual networks for deployment using a single bit for each weight"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "memory efficient implementation of densenets"
    - "lifetime based memory management for distributed data processing systems"
    - "database meets deep learning challenges and opportunities"
    - "eie efficient inference engine on compressed deep neural network"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-7 (induced by source paper "chaos a parallelization scheme for training convolutional neural networks on intel xeon phi"):
  with members:
    - "benchmarking state of the art deep learning software tools"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "one weird trick for parallelizing convolutional neural networks"
    - "multi gpu training of convnets"
    - "comparative performance analysis of intel xeon phi gpu and cpu"
    - "optimizing the mapreduce framework on intel xeon phi coprocessor"

Connected through hyperedge HE-8 (induced by source paper "pcamp performance comparison of machine learning packages on the edges"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "benchmarking state of the art deep learning software tools"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "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.LG(Machine Learning)