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: "intrinsic image transformation via scale space decomposition"
  Abstract: We introduce a new network structure for decomposing an image into its intrinsic albedo and shading. We treat this as an image-to-image transformation problem and explore the scale space of the input and output. By expanding the output images (albedo and shading) into their Laplacian pyramid components, we develop a multi-channel network structure that learns the image-to-image transformation function in successive frequency bands in parallel, within each channel is a fully convolutional neural network with skip connections. This network structure is general and extensible, and has demonstrated excellent performance on the intrinsic image decomposition problem. We evaluate the network on two benchmark datasets: the MPI-Sintel dataset and the MIT Intrinsic Images dataset. Both quantitative and qualitative results show our model delivers a clear progression over state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "shadingnet image intrinsics by fine grained shading decomposition"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "learning intrinsic image decomposition from watching the world"
    - "joint learning of intrinsic images and semantic segmentation"
    - "unsupervised deep single image intrinsic decomposition using illumination varying image sequences"
    - "squeeze and excitation networks"
    - "self supervised intrinsic image decomposition"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"

Connected through hyperedge HE-2 (induced by source paper "when single event upset meets deep neural networks observations explorations and remedies"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "learning phase competition for traffic signal control"
    - "neural color transfer between images"
    - "on the robustness of convolutional neural networks to internal architecture and weight perturbations"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

Connected through hyperedge HE-3 (induced by source paper "direction aware spatial context features for shadow detection and removal"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "fast shadow detection from a single image using a patched convolutional neural network"
    - "direction aware spatial context features for shadow detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "training region based object detectors with online hard example mining"
    - "pyramid scene parsing network"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"

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)