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: "deepwheat estimating phenotypic traits from crop images with deep learning"
  Abstract: In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.

Connected through hyperedge HE-1 (induced by source paper "object counting with small datasets of large images"):
  with members:
    - "improving object counting with heatmap regulation"
    - "switching convolutional neural network for crowd counting"
    - "count ception counting by fully convolutional redundant counting"
    - "leveraging multiple datasets for deep leaf counting"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "drone based object counting by spatially regularized regional proposal network"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "locating objects without bounding boxes"):
  with members:
    - "on the convergence of adam and beyond"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "spatiotemporal modeling for crowd counting in videos"
    - "mask r cnn"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-3 (induced by source paper "global sum pooling a generalization trick for object counting with small datasets of large images"):
  with members:
    - "improving object counting with heatmap regulation"
    - "switching convolutional neural network for crowd counting"
    - "count ception counting by fully convolutional redundant counting"
    - "leveraging multiple datasets for deep leaf counting"
    - "drone based object counting by spatially regularized regional proposal network"
    - "leaf counting with deep convolutional and deconvolutional networks"
    - "mask r cnn"
    - "crowdnet a deep convolutional network for dense crowd counting"

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)