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: "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
  Abstract: Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. #R##N#Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. #R##N#We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. #R##N#We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

Connected through hyperedge HE-1 (induced by source paper "an auxiliary task for learning nuclei segmentation in 3d microscopy images"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy"):
  with members:
    - "synaptic partner assignment using attentional voxel association networks"
    - "siamese encoding and alignment by multiscale learning with self supervision"
    - "chunkflow distributed hybrid cloud processing of large 3d images by convolutional nets"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"
    - "synaptic partner prediction from point annotations in insect brains"
    - "joint deformable registration of large em image volumes a matrix solver approach"
    - "learned watershed end to end learning of seeded segmentation"

Connected through hyperedge HE-3 (induced by source paper "synaptic partner assignment using attentional voxel association networks"):
  with members:
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"
    - "synaptic partner prediction from point annotations in insect brains"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "an error detection and correction framework for connectomics"
    - "toward streaming synapse detection with compositional convnets"

Connected through hyperedge HE-4 (induced by source paper "leveraging domain knowledge to improve em image segmentation with lifted multicuts"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "cross classification clustering an efficient multi object tracking technique for 3 d instance segmentation in connectomics"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "morphological error detection in 3d segmentations"
    - "u net convolutional networks for biomedical image segmentation"
    - "analysis and optimization of graph decompositions by lifted multicuts"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "scikit image image processing in python"

Connected through hyperedge HE-5 (induced by source paper "synthetic patches real images screening for centrosome aberrations in em images of human cancer cells"):
  with members:
    - "attention based deep multiple instance learning"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "synthetic data for text localisation in natural images"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "fully automatic synapse prediction and validation on a large data set"):
  with members:
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"
    - "identifying synapses using deep and wide multiscale recursive networks"
    - "deep and wide multiscale recursive networks for robust image labeling"

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)