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: "ssemnet serial section electron microscopy image registration using a spatial transformer network with learned features"
  Abstract: The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane contains densely packed structures that vary from one section to the next, which makes matching features across images a challenge. Advances in deep learning has resulted in unprecedented performance in similar computer vision problems, but to our knowledge, they have not been successfully applied to ssEM image co-registration. In this paper, we introduce a novel deep network model that combines a spatial transformer for image deformation and a convolutional autoencoder for unsupervised feature learning for robust ssEM image alignment. This results in improved accuracy and robustness while requiring substantially less user intervention than conventional methods. We evaluate our method by comparing registration quality across several datasets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on reducing negative jacobian determinant of the deformation predicted by deep registration networks"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "inverse consistent deep networks for unsupervised deformable image registration"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "unsupervised end to end learning for deformable medical image registration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "siamese encoding and alignment by multiscale learning with self supervision"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "optical flow estimation using a spatial pyramid network"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "u net convolutional networks for biomedical image segmentation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised learning of metric aggregations for deformable image registration"):
  with members:
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "slice to volume medical image registration a survey"
    - "deformable registration through learning of context specific metric aggregation"
    - "a deep metric for multimodal registration"
    - "spatial transformer networks"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "non rigid image registration using self supervised fully convolutional networks without training data"):
  with members:
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "quicksilver fast predictive image registration a deep learning approach"

Connected through hyperedge HE-6 (induced by source paper "non rigid image registration using fully convolutional networks with deep self supervision"):
  with members:
    - "quicksilver fast predictive image registration a deep learning approach"
    - "tensorflow a system for large scale machine learning"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

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)