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: "fast shadow detection from a single image using a patched convolutional neural network"
  Abstract: In recent years, various shadow detection methods from a single image have been proposed and used in vision systems; however, most of them are not appropriate for the robotic applications due to the expensive time complexity. This paper introduces a fast shadow detection method using a deep learning framework, with a time cost that is appropriate for robotic applications. In our solution, we first obtain a shadow prior map with the help of multi-class support vector machine using statistical features. Then, we use a semantic- aware patch-level Convolutional Neural Network that efficiently trains on shadow examples by combining the original image and the shadow prior map. Experiments on benchmark datasets demonstrate the proposed method significantly decreases the time complexity of shadow detection, by one or two orders of magnitude compared with state-of-the-art methods, without losing accuracy.

Connected through hyperedge HE-1 (induced by source paper "conditional gans for multi illuminant color constancy revolution or yet another approach"):
  with members:
    - "video to video synthesis"
    - "multimodal unsupervised image to image translation"
    - "artificial color constancy via googlenet with angular loss function"
    - "direction aware spatial context features for shadow detection and removal"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "direction aware spatial context features for shadow detection and removal"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "intrinsic image transformation via scale space decomposition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "direction aware spatial context features for shadow detection"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "pyramid scene parsing network"
    - "holistically nested edge detection"
    - "shadow optimization from structured deep edge detection"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)