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: "superhuman accuracy on the snemi3d connectomics challenge"
  Abstract: For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images. Recent years have seen great improvements in accuracy, as evidenced by submissions to the SNEMI3D benchmark challenge. Here we report the first submission to surpass the estimate of human accuracy provided by the SNEMI3D leaderboard. A variant of 3D U-Net is trained on a primary task of predicting affinities between nearest neighbor voxels, and an auxiliary task of predicting long-range affinities. The training data is augmented by simulated image defects. The nearest neighbor affinities are used to create an oversegmentation, and then supervoxels are greedily agglomerated based on mean affinity. The resulting SNEMI3D score exceeds the estimate of human accuracy by a large margin. While one should be cautious about extrapolating from the SNEMI3D benchmark to real-world accuracy of large-scale neural circuit reconstruction, our result inspires optimism that the goal of full automation may be realizable in the future.

Connected through hyperedge HE-1 (induced by source paper "non local u net for biomedical image segmentation"):
  with members:
    - "smoothed dilated convolutions for improved dense prediction"
    - "non local neural networks"
    - "attention is all you need"
    - "identity mappings in deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "feature pyramid networks for object detection"

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 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-3 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "instance segmentation by deep coloring"
    - "affinity derivation and graph merge for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "cell detection with star convex polygons"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "recurrent pixel embedding for instance grouping"
    - "pixelwise instance segmentation with a dynamically instantiated network"

Connected through hyperedge HE-4 (induced by source paper "improving electron micrograph signal to noise with an atrous convolutional encoder decoder"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "beating the world s best at super smash bros with deep reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "revisiting distributed synchronous sgd"

Connected through hyperedge HE-5 (induced by source paper "the semantic mutex watershed for efficient bottom up semantic instance segmentation"):
  with members:
    - "panoptic segmentation"
    - "leveraging domain knowledge to improve microscopy image segmentation with lifted multicuts"
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "panoptic feature pyramid networks"
    - "learning a discriminative feature network for semantic segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-6 (induced by source paper "a hierarchical probabilistic u net for modeling multi scale ambiguities"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "tensormask a foundation for dense object segmentation"
    - "instance segmentation of biological images using harmonic embeddings"
    - "semantic image synthesis with spatially adaptive normalization"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "instance segmentation by deep coloring"

Connected through hyperedge HE-7 (induced by source paper "hadanets flexible quantization strategies for neural networks"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "universal deep neural network compression"
    - "compressing neural networks using the variational information bottleneck"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"
    - "the high dimensional geometry of binary neural networks"

Connected through hyperedge HE-8 (induced by source paper "assisting human experts in the interpretation of their visual process a case study on assessing copper surface adhesive potency"):
  with members:
    - "boosting segmentation with weak supervision from image to image translation"
    - "a multi task u net for segmentation with lazy labels"
    - "knowing where to look analysis on attention of visual question answering system"
    - "deep convolutional neural network design patterns"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "image captioning with semantic attention"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "understanding neural networks through deep visualization"

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)