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: "flood filling networks"
  Abstract: State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments. Prior work has varied the complexity and approach employed in these two steps, including the incorporation of multi-layer neural networks to perform boundary prediction, and the use of global optimizations during pixel clustering. We propose a unified and end-to-end trainable machine learning approach, flood-filling networks, in which a recurrent 3d convolutional network directly produces individual segments from a raw image. The proposed approach robustly segments images with an unknown and variable number of objects as well as highly variable object sizes. We demonstrate the approach on a challenging 3d image segmentation task, connectomic reconstruction from volume electron microscopy data, on which flood-filling neural networks substantially improve accuracy over other state-of-the-art methods. The proposed approach can replace complex multi-step segmentation pipelines with a single neural network that is learned end-to-end.

Connected through hyperedge HE-1 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction"):
  with members:
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "on the convergence of adam and beyond"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"

Connected through hyperedge HE-2 (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-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 cleft segmentation in non isotropic volume electron microscopy of the complete drosophila brain"
    - "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"

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"
    - "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"
    - "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 "synaptic partner prediction from point annotations in insect brains"):
  with members:
    - "fully automatic synapse prediction and validation on a large data set"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "vesicle volumetric evaluation of synaptic interfaces using computer vision at large scale"

Connected through hyperedge HE-6 (induced by source paper "morphological error detection in 3d segmentations"):
  with members:
    - "toward streaming synapse detection with compositional convnets"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "deep tensor convolution on multicores"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "learned watershed end to end learning of seeded segmentation"):
  with members:
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "asynchronous methods for deep reinforcement learning"
    - "deep watershed transform for instance segmentation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "continuous control with deep reinforcement learning"
    - "multi scale context aggregation by dilated convolutions"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"

Connected through hyperedge HE-8 (induced by source paper "beyond the pixel wise loss for topology aware delineation"):
  with members:
    - "active learning and proofreading for delineation of curvilinear structures"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "deep retinal image understanding"
    - "stacked hourglass networks for human pose estimation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "densecap fully convolutional localization networks for dense captioning"

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)