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: "cross domain cnn for hyperspectral image classification"
  Abstract: In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to effectively learn high-capacity Convolutional Neural Networks (CNNs). To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral datasets. The network also contains the non-shared portions designed to handle the dataset specific spectral characteristics and the associated classification tasks. Our approach is the first attempt to learn a CNN for multiple hyperspectral datasets, in an end-to-end fashion. Moreover, we have experimentally shown that the proposed network trained on three of the widely used datasets outperform all the baseline networks which are trained on single dataset.

Connected through hyperedge HE-1 (induced by source paper "is pretraining necessary for hyperspectral image classification"):
  with members:
    - "rethinking imagenet pre training"
    - "exploring the limits of weakly supervised pretraining"
    - "dod cnn doubly injecting object information for event recognition"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "iod cnn integrating object detection networks for event recognition"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-2 (induced by source paper "discovering and generating hard examples for training a red tide detector"):
  with members:
    - "deep video generation prediction and completion of human action sequences"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "future frame prediction for anomaly detection a new baseline"
    - "residual conv deconv grid network for semantic segmentation"
    - "contextual deep cnn based hyperspectral classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "me r cnn multi expert r cnn for object detection"):
  with members:
    - "s dod cnn doubly injecting spatially preserved object information for event recognition"
    - "growing a brain fine tuning by increasing model capacity"
    - "is pretraining necessary for hyperspectral image classification"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "dod cnn doubly injecting object information for event recognition"
    - "detecting and recognizing human object interactions"
    - "soft nms improving object detection with one line of code"

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)