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: "combinatorial energy learning for image segmentation"
  Abstract: We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.

Connected through hyperedge HE-1 (induced by source paper "patchperpix for instance segmentation"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "tensormask a foundation for dense object segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "cell detection with star convex polygons"
    - "semi convolutional operators for instance segmentation"
    - "learned watershed end to end learning of seeded segmentation"
    - "instance sensitive fully convolutional networks"

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 "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-4 (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-5 (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"
    - "flood filling networks"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"):
  with members:
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "mahotas open source software for scriptable computer vision"

Connected through hyperedge HE-7 (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-8 (induced by source paper "flood filling networks"):
  with members:
    - "identity mappings in deep residual networks"
    - "learning to refine object segments"
    - "pixel recurrent neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "pixel wise deep learning for contour detection"
    - "deep residual learning for image recognition"
    - "image segmentation by size dependent single linkage clustering of a watershed basin graph"

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)