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: "globenet convolutional neural networks for typhoon eye tracking from remote sensing imagery"
  Abstract: Advances in remote sensing technologies have made it possible to use high-resolution visual data for weather observation and forecasting tasks. We propose the use of multi-layer neural networks for understanding complex atmospheric dynamics based on multichannel satellite images. The capability of our model was evaluated by using a linear regression task for single typhoon coordinates prediction. A specific combination of models and different activation policies enabled us to obtain an interesting prediction result in the northeastern hemisphere (ENH).

Connected through hyperedge HE-1 (induced by source paper "hpc ai500 a benchmark suite for hpc ai systems"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "tbd benchmarking and analyzing deep neural network training"
    - "scaling grpc tensorflow on 512 nodes of cori supercomputer"
    - "benchmarking state of the art deep learning software tools"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"

Connected through hyperedge HE-2 (induced by source paper "exascale deep learning for climate analytics"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "chainermn scalable distributed deep learning framework"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "psique next sequence prediction of satellite images using a convolutional sequence to sequence network"):
  with members:
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "tensorflow a system for large scale machine learning"
    - "spatio temporal video autoencoder with differentiable memory"
    - "unsupervised learning of video representations using lstms"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "attention based models for speech recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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.NE(Neural and Evolutionary Computing)