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: "tree species identification from bark images using convolutional neural networks"
  Abstract: Tree species identification using images of the bark is a challenging problem that could help in tasks such as drone navigation in forest environment and autonomous forest inventory management. It also brings more value to harvesting operations as it leads to greater market values of trees. While the recent progress in deep learning showed its effectiveness for visual classification, it cannot currently be used for bark classification due to a lack of dataset. In this work, we present a novel dataset containing more than 23 000 high-resolution bark images from 23 different species and establish a benchmark using deep learning. We obtain an accuracy of 93.88% and show the possibility of using a majority voting approach on all the images of a tree to obtain 97.81% accuracy. We also perform experiments indicating that it is more important to collect a large amount of trees over a large quantity of image and that images of a single tree should be taken at different locations.

Connected through hyperedge HE-1 (induced by source paper "mango tree net a fully convolutional network for semantic segmentation and individual crown detection of mango trees"):
  with members:
    - "deep learning versus obia for scattered shrub detection with google earth imagery ziziphus lotus as case study"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "automatic 3d mapping for tree diameter measurements in inventory operations"):
  with members:
    - "supervised fitting of geometric primitives to 3d point clouds"

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)