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: "deep and wide multiscale recursive networks for robust image labeling"
  Abstract: Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a `wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels ($54^3$) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks.

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 "brain tumor segmentation with deep neural networks"):
  with members:
    - "simultaneous detection and segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "recurrent convolutional neural networks for scene parsing"
    - "practical recommendations for gradient based training of deep architectures"
    - "representation learning a review and new perspectives"
    - "a generalized mean field algorithm for variational inference in exponential families"

Connected through hyperedge HE-3 (induced by source paper "deep learning convolutional networks for multiphoton microscopy vasculature segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "theano a python framework for fast computation of mathematical expressions"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "sparse convolutional networks using the permutohedral lattice"
    - "an iterative convolutional neural network algorithm improves electron microscopy image segmentation"

Connected through hyperedge HE-4 (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"
    - "identifying synapses using deep and wide multiscale recursive networks"

Connected through hyperedge HE-5 (induced by source paper "recursive training of 2d 3d convolutional networks for neuronal boundary detection"):
  with members:
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "fully convolutional networks for semantic segmentation"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-6 (induced by source paper "combinatorial energy learning for image segmentation"):
  with members:
    - "fully connected deep structured networks"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"
    - "learned versus hand designed feature representations for 3d agglomeration"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-7 (induced by source paper "what properties are desirable from an electron microscopy segmentation algorithm"):
  with members:
    - "a context aware delayed agglomeration framework for electron microscopy segmentation"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"
    - "fast image scanning with deep max pooling convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "a tolerant edit distance for evaluation and training of electron microscopy reconstruction algorithms"):
  with members:
    - "small sample learning of superpixel classifiers for em segmentation extended version"
    - "machine learning of hierarchical clustering to segment 2d and 3d images"
    - "objective criteria for the evaluation of clustering methods"

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)