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: "automatic detection of neurons in neun stained histological images of human brain"
  Abstract: Abstract   In this paper we propose a new method for automatic detection of neurons in histological sections of the human brain cortex, based on anisotropic diffusion. The anisotropic diffusion is modeled using a partial differential equation (PDE) and is applied to high resolution microscopy images of the brain in order to detect neurons. We also present a novel approach for PDE-model parameter optimization. Due to the issue of inter-observer variability, three human experts have manually annotated neurons in the image dataset on which the proposed method was trained. The average correlation in neuron detection between the human experts was    86  .  88  %   , while the average correlation between the proposed method and the human experts is    88  .  79  %   , which shows that the proposed method’s performance is equal to that of human experts. Moreover, the proposed automatic method provides consistent and reproducible results on all sections and is much faster than human raters or other automatic methods. Additionally, the proposed method’s output was verified by a human expert and has correctly distinguished    95  .  41  %    of neurons in the test images.

Connected through hyperedge HE-1 (induced by source paper "computational analysis of laminar structure of the human cortex based on local neuron features"):
  with members:
    - "consistent individualized feature attribution for tree ensembles"
    - "catboost gradient boosting with categorical features support"
    - "a unified approach to interpreting model predictions"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "imagej2 imagej for the next generation of scientific image data"
    - "xgboost a scalable tree boosting system"
    - "comment on clustering by fast search and find of density peaks"

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)