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: "identifying synapses using deep and wide multiscale recursive networks"
  Abstract: In this work, we propose a learning framework for identifying synapses using a deep and wide multi-scale recursive (DAWMR) network, previously considered in image segmentation applications. We apply this approach on electron microscopy data from invertebrate fly brain tissue. By learning features directly from the data, we are able to achieve considerable improvements over existing techniques that rely on a small set of hand-designed features. We show that this system can reduce the amount of manual annotation required, in both acquisition of training data as well as verification of inferred detections.

Connected through hyperedge HE-1 (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 cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "synaptic partner prediction from point annotations in insect brains"
    - "joint deformable registration of large em image volumes a matrix solver approach"

Connected through hyperedge HE-2 (induced by source paper "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "mask r cnn"
    - "training recurrent neural networks by diffusion"
    - "fully automatic synapse prediction and validation on a large data set"
    - "flood filling networks"
    - "sshmt semi supervised hierarchical merge tree for electron microscopy image segmentation"

Connected through hyperedge HE-3 (induced by source paper "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "anisotropic em segmentation by 3d affinity learning and agglomeration"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "a context aware delayed agglomeration framework for electron microscopy segmentation"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"

Connected through hyperedge HE-5 (induced by source paper "fully automatic synapse prediction and validation on a large data set"):
  with members:
    - "synaptic cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"
    - "deep and wide multiscale recursive networks for robust image labeling"

Connected through hyperedge HE-6 (induced by source paper "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"):
  with members:
    - "deep convolutional networks for pancreas segmentation in ct imaging"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"
    - "fast image scanning with deep max pooling convolutional neural networks"

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)