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: "underwater fish species classification using convolutional neural network and deep learning"
  Abstract: The target of this paper is to recommend a way for Automated classification of Fish species. A high accuracy fish classification is required for greater understanding of fish behavior in Ichthyology and by marine biologists. Maintaining a ledger of the number of fishes per species and marking the endangered species in large and small water bodies is required by concerned institutions. Majority of available methods focus on classification of fishes outside of water because underwater classification poses challenges such as background noises, distortion of images, the presence of other water bodies in images, image quality and occlusion. This method uses a novel technique based on Convolutional Neural Networks, Deep Learning and Image Processing to achieve an accuracy of 96.29%. This method ensures considerably discrimination accuracy improvements than the previously proposed methods.

Connected through hyperedge HE-1 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "underwater fish detection with weak multi domain supervision"
    - "datasets for face and object detection in fisheye images"
    - "spherical cnns on unstructured grids"
    - "spherical u net on cortical surfaces methods and applications"
    - "tagslam robust slam with fiducial markers"
    - "convolutions on spherical images"
    - "reprojection r cnn a fast and accurate object detector for 360 images"

Connected through hyperedge HE-2 (induced by source paper "deep learning for plankton and coral classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation"
    - "self normalizing neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "underwater fish detection using deep learning for water power applications"):
  with members:
    - "yolov3 an incremental improvement"
    - "assessing fish abundance from underwater video using deep neural networks"
    - "differentiating objects by motion joint detection and tracking of small flying objects"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

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)