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: "star convex polyhedra for 3d object detection and segmentation in microscopy"
  Abstract: Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects. Although many automated methods for these tasks exist, they often struggle for images with low signal-to-noise ratios and/or dense packing of nuclei. It was recently shown for 2D microscopy images that these issues can be alleviated by training a neural network to directly predict a suitable shape representation (star-convex polygon) for cell nuclei. In this paper, we adopt and extend this approach to 3D volumes by using star-convex polyhedra to represent cell nuclei and similar shapes. To that end, we overcome the challenges of 1) finding parameter-efficient star-convex polyhedra representations that can faithfully describe cell nuclei shapes, 2) adapting to anisotropic voxel sizes often found in fluorescence microscopy datasets, and 3) efficiently computing intersections between pairs of star-convex polyhedra (required for non-maximum suppression). Although our approach is quite general, since star-convex polyhedra subsume common shapes like bounding boxes and spheres as special cases, our focus is on accurate detection and segmentation of cell nuclei. That that end, we demonstrate on two challenging datasets that our approach (StarDist-3D) leads to superior results when compared to classical and deep-learning based methods.

Connected through hyperedge HE-1 (induced by source paper "patchperpix for instance segmentation"):
  with members:
    - "ssap single shot instance segmentation with affinity pyramid"
    - "tensormask a foundation for dense object segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "cell detection with star convex polygons"
    - "semi convolutional operators for instance segmentation"
    - "learned watershed end to end learning of seeded segmentation"
    - "instance sensitive fully convolutional networks"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images"):
  with members:
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "deep watershed transform for instance segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "mahotas open source software for scriptable computer vision"

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)