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: "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
  Abstract: We present a deep structured learning method for neuron segmentation from 3D electron microscopy (EM) which improves significantly upon the state of the art in terms of accuracy and scalability. Our method consists of a 3D U-Net classifier predicting affinity graphs on voxels, followed by iterative region agglomeration. We train the U-Net using a new structured loss based on MALIS that encourages topological correctness. Our extension consists of two parts: First, an $O(n\log(n))$ method to compute the loss gradient, improving over the originally proposed $O(n^2)$ algorithm. Second, we compute the gradient in two separate passes to avoid spurious contributions in early training stages. Our affinity predictions are accurate enough that simple agglomeration outperforms more involved methods used earlier on inferior predictions. We present results on three datasets (CREMI, FIB, and SegEM) of different imaging techniques and animals and achieve improvements over previous results of 27%, 15%, and 250%. Our findings suggest that a single 3D segmentation strategy can be applied to both isotropic and anisotropic EM data. The runtime of our method scales with $O(n)$ in the size of the volume and achieves a throughput of about 2.6 seconds per megavoxel, allowing processing of very large datasets.

Connected through hyperedge HE-1 (induced by source paper "scaling distributed training of flood filling networks on hpc infrastructure for brain mapping"):
  with members:
    - "an empirical model of large batch training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-2 (induced by source paper "topology preserving deep image segmentation"):
  with members:
    - "a topological regularizer for classifiers via persistent homology"
    - "sliced wasserstein kernel for persistence diagrams"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep learning with topological signatures"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "parallel separable 3d convolution for video and volumetric data understanding"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "densely connected convolutional networks"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "a fixed point model for pancreas segmentation in abdominal ct scans"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (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"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "cnn based preprocessing to optimize watershed based cell segmentation in 3d confocal microscopy images"):
  with members:
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "learned watershed end to end learning of seeded segmentation"
    - "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"
    - "segment3d a web based application for collaborative segmentation of 3d images used in the shoot apical meristem"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep watershed transform for instance segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (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"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "identifying synapses using deep and wide multiscale recursive networks"

Connected through hyperedge HE-7 (induced by source paper "an error detection and correction framework for connectomics"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "residual conv deconv grid network for semantic segmentation"
    - "semantic instance segmentation via deep metric learning"
    - "end to end instance segmentation and counting with recurrent attention"
    - "learning to segment object candidates"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)