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: "imagej2 imagej for the next generation of scientific image data"
  Abstract: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. #R##N#We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

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:
    - "automatic detection of neurons in neun stained histological images of human brain"
    - "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"
    - "xgboost a scalable tree boosting system"
    - "comment on clustering by fast search and find of density peaks"

Connected through hyperedge HE-2 (induced by source paper "sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning"):
  with members:
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "scenery flexible virtual reality visualisation on the java vm"):
  with members:
    - "inviwo a visualization system with usage abstraction levels"
    - "syglass interactive exploration of multidimensional images using virtual reality head mounted displays"
    - "deep shading convolutional neural networks for screen space shading"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"):
  with members:
    - "classification of mri data using deep learning and gaussian process based model selection"
    - "retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network"
    - "relaynet retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "richer convolutional features for edge detection"
    - "medical image denoising using convolutional denoising autoencoders"
    - "multiframe denoising of high speed optical coherence tomography data using interframe and intraframe priors"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers"):
  with members:
    - "jansen midas a multi level photomicrograph segmentation software based on isotropic undecimated wavelets"
    - "scikit image image processing in python"
    - "an automatic method for segmentation of fission tracks in epidote crystal photomicrographs"
    - "the numpy array a structure for efficient numerical computation"

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.SE(Software Engineering)