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: "prediction of sea surface temperature using long short term memory"
  Abstract: This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean. The SST prediction problem is formulated as a time series regression problem. The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer. The LSTM layer is utilized to model the time series relationship. The full-connected layer is utilized to map the output of the LSTM layer to a final prediction. The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method. The prediction accuracy is also tested on the SST anomaly data. In addition, the model’s online updated characteristics are presented.

Connected through hyperedge HE-1 (induced by source paper "stconvs2s spatiotemporal convolutional sequence to sequence network for weather forecasting"):
  with members:
    - "recurrent convolutions for causal 3d cnns"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "convolutional sequence to sequence learning"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"

Connected through hyperedge HE-2 (induced by source paper "autoencoder based residual deep networks for robust regression prediction and spatiotemporal estimation"):
  with members:
    - "multi aspect aware bidirectional lstm networks for synthetic aperture radar target recognition"
    - "identity mappings in deep residual networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "predicting blood glucose with an lstm and bi lstm based deep neural network"):
  with members:
    - "an lstm network for highway trajectory prediction"
    - "a deep learning approach to diabetic blood glucose prediction"

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)