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: "a leaf recognition algorithm for plant classification using probabilistic neural network"
  Abstract: In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.

Connected through hyperedge HE-1 (induced by source paper "deepweeds a multiclass weed species image dataset for deep learning"):
  with members:
    - "squeeze and excitation networks"
    - "crafting gbd net for object detection"
    - "deep residual learning for image recognition"
    - "deep plant plant identification with convolutional neural networks"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "real world plant species identification based on deep convolutional neural networks and visual attention"):
  with members:
    - "learning to select pre trained deep representations with bayesian evidence framework"
    - "deep residual learning for image recognition"
    - "some improvements on deep convolutional neural network based image classification"
    - "visual saliency based on scale space analysis in the frequency domain"

Connected through hyperedge HE-3 (induced by source paper "shape only features for plant leaf identification"):
  with members:
    - "treelogy a novel tree classifier utilizing deep and hand crafted representations"
    - "leaf classification using shape color and texture features"
    - "a model of plant identification system using glcm lacunarity and shen features"
    - "neural network application on foliage plant identification"

Connected through hyperedge HE-4 (induced by source paper "minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy"):
  with members:
    - "fast asymmetric fronts propagation for image segmentation"
    - "global minimum for a finsler elastica minimal path approach"
    - "vtrails inferring vessels with geodesic connectivity trees"

Connected through hyperedge HE-5 (induced by source paper "a rapidly deployable classification system using visual data for the application of precision weed management"):
  with members:
    - "towards unsupervised weed scouting for agricultural robotics"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "blind deconvolution by a steepest descent algorithm on a quotient manifold"):
  with members:
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "self calibration and biconvex compressive sensing"
    - "blind recovery of sparse signals from subsampled convolution"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-7 (induced by source paper "treelogy a novel tree classifier utilizing deep and hand crafted representations"):
  with members:
    - "leaf classification using shape color and texture features"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "image projective invariants"):
  with members:
    - "naturally combined shape color moment invariants under affine transformations"
    - "isomorphism between differential and moment invariants under affine transform"
    - "understanding image representations by measuring their equivariance and equivalence"
    - "differential invariants under gamma correction"

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.AI(Artificial Intelligence)