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: "remote sensing image classification exploiting multiple kernel learning"
  Abstract: We propose a strategy for land use classification, which exploits multiple kernel learning (MKL) to automatically determine a suitable combination of a set of features without requiring any heuristic knowledge about the classification task. We present a novel procedure that allows MKL to achieve good performance in the case of small training sets. Experimental results on publicly available data sets demonstrate the feasibility of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "visual descriptors for content based retrieval of remote sensing images"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "evaluating color texture descriptors under large variations of controlled lighting conditions"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "deep learning in neural networks"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "learning multi scale deep features for high resolution satellite image classification"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"
    - "some improvements on deep convolutional neural network based image classification"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "adaptive deep pyramid matching for remote sensing scene classification"):
  with members:
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "representation learning a review and new perspectives"

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)