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 analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
  Abstract: The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "batch size influence on performance of graphic and tensor processing units during training and inference phases"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "revisiting small batch training for deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "a gpu outperforming fpga accelerator architecture for binary convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"

Connected through hyperedge HE-3 (induced by source paper "an orchestrated empirical study on deep learning frameworks and platforms"):
  with members:
    - "deep learning towards mobile applications"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "combinatorial testing for deep learning systems"
    - "deep learning with dynamic computation graphs"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "benchmarking state of the art deep learning software tools"

Connected through hyperedge HE-4 (induced by source paper "chest x ray analysis of tuberculosis by deep learning with segmentation and augmentation"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "deep learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer"
    - "dimensionality reduction in deep learning for chest x ray analysis of lung cancer"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-5 (induced by source paper "dimensionality reduction in deep learning for chest x ray analysis of lung cancer"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "deep learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "deep learning for fatigue estimation on the basis of multimodal human machine interactions"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

Connected through hyperedge HE-7 (induced by source paper "music transcription by deep learning with data and artificial semantic augmentation"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "an experimental analysis of the entanglement problem in neural network based music transcription systems"
    - "automatized generation of alphabets of symbols"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "slice to volume medical image registration a survey"

Connected through hyperedge HE-8 (induced by source paper "performance analysis of open source machine learning frameworks for various parameters in single threaded and multi threaded modes"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "automatized generation of alphabets of symbols"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "tensorflow a system for large scale machine learning"

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)