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: "performance analysis of open source machine learning frameworks for various parameters 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 Also, we present the results of testing neural networks architectures on H2O platform for various activation functions, stopping metrics, and other parameters of machine learning algorithm. It was demonstrated for the use case of MNIST database of handwritten digits in single-threaded mode that blind selection of these parameters can hugely increase (by 2–3 orders) the runtime without the significant increase of precision. This result can have crucial influence for optimization of available and new machine learning methods, especially for image recognition problems.

Connected through hyperedge HE-1 (induced by source paper "response transformation and profit decomposition for revenue uplift modeling"):
  with members:
    - "pessimistic uplift modeling"
    - "xgboost a scalable tree boosting system"
    - "a decision theoretic approach to targeted advertising"
    - "smote synthetic minority over sampling technique"

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"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (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"
    - "comparative analysis of open source frameworks for machine learning with use case 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-4 (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"
    - "comparative analysis of open source frameworks for machine learning with use case 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-5 (induced by source paper "deep learning with lung segmentation and bone shadow exclusion techniques for chest x ray analysis of lung cancer"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "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"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"):
  with members:
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"
    - "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"
    - "generating and estimating nonverbal alphabets for situated and multimodal communications"
    - "multi parametric statistical method for estimation of accumulated fatigue by sensors in ordinary gadgets"
    - "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 "gamification for education of the digitally native generation by means of virtual reality augmented reality machine learning and brain computing interfaces in museums"):
  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"
    - "music transcription by deep learning with data and artificial semantic augmentation"

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)