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: "automatic salt deposits segmentation a deep learning approach"
  Abstract: One of the most important applications of seismic reflection is the hydrocarbon exploration which is closely related to salt deposits analysis. This problem is very important even nowadays due to it's non-linear nature. Taking into account the recent developments in deep learning networks TGS-NOPEC Geophysical Company hosted the Kaggle competition for salt deposits segmentation problem in seismic image data. In this paper, we demonstrate the great performance of several novel deep learning techniques merged into a single neural network which achieved the 27th place (top 1%) in the mentioned competition. Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as our base architecture, we implemented Spatial-Channel Squeeze & Excitation, Lovasz loss, CoordConv and Hypercolumn methods. The source code for our solution is made publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "sfa gtm seismic facies analysis based on generative topographic map and rbf"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep unsupervised learning of visual similarities"
    - "unsupervised representation learning by sorting sequences"
    - "deep unsupervised similarity learning using partially ordered sets"

Connected through hyperedge HE-2 (induced by source paper "time series anomaly detection using convolutional neural networks and transfer learning"):
  with members:
    - "exploring the limits of weakly supervised pretraining"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "multivariate industrial time series with cyber attack simulation fault detection using an lstm based predictive data model"

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)