human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "computational analysis of laminar structure of the human cortex based on local neuron features"):
  Abstract: In this paper, we present a novel method for analysis and segmentation of laminar structure of the cortex based on tissue characteristics whose change across the gray matter facilitates distinction between cortical layers. We develop and analyze features of individual neurons to investigate changes in architectonic differentiation and present a novel high-performance, automated tree-ensemble method trained on data manually labeled by three human investigators. From the location and basic measures of neurons, more complex features are developed and used in machine learning models for automatic segmentation of cortical layers. Tree ensembles are used on data manually labeled by three human experts. The most accurate classification results were obtained by training three models separately and creating another ensemble by combining probability outputs for final neuron layer classification. Measurement of importances of developed neuron features on both global model level and individual prediction level are obtained.

Includes members:
  Member "automatic detection of neurons in neun stained histological images of human brain":
    also connected through other hyperedges:
      (none)
  Member "consistent individualized feature attribution for tree ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating interpretable poverty maps using object detection in satellite images")
  Member "catboost gradient boosting with categorical features support":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep differentiable forest with sparse attention for the tabular data")
  Member "a unified approach to interpreting model predictions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "towards better analysis of machine learning models a visual analytics perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "imagej2 imagej for the next generation of scientific image data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "comment on clustering by fast search and find of density peaks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "leading tree in dpclus and its impact on building hierarchies")

Question: Please tell me which class the source paper 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)