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: "learning deep structured network for weakly supervised change detection"
  Abstract: Conventional change detection methods require a large number of images to learn background models or depend on tedious pixel-level labeling by humans. In this paper, we present a weakly supervised approach that needs only image-level labels to simultaneously detect and localize changes in a pair of images. To this end, we employ a deep neural network with DAG topology to learn patterns of change from image-level labeled training data. On top of the initial CNN activations, we define a CRF model to incorporate the local differences and context with the dense connections between individual pixels. We apply a constrained mean-field algorithm to estimate the pixel-level labels, and use the estimated labels to update the parameters of the CNN in an iterative EM framework. This enables imposing global constraints on the observed foreground probability mass function. Our evaluations on four benchmark datasets demonstrate superior detection and localization performance.

Connected through hyperedge HE-1 (induced by source paper "a novel inspection system for variable data printing using deep learning"):
  with members:
    - "anonet weakly supervised anomaly detection in textured surfaces"
    - "slum segmentation and change detection a deep learning approach"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning"
    - "spot the difference by object detection"
    - "fully convolutional siamese networks for change detection"
    - "light weight refinenet for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"

Connected through hyperedge HE-3 (induced by source paper "learning to measure change fully convolutional siamese metric networks for scene change detection"):
  with members:
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "depth adaptive deep neural network for semantic segmentation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation aware convolutional networks using local attention masks"
    - "how far can you get by combining change detection algorithms"
    - "deformable convolutional networks"
    - "semantic instance segmentation via deep metric learning"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised silhouette based semantic change detection"):
  with members:
    - "panoptic segmentation"
    - "robust change captioning"
    - "m4cd a robust change detection method for intelligent visual surveillance"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning to describe differences between pairs of similar images"
    - "fully convolutional siamese networks for change detection"
    - "high resolution semantic change detection"

Connected through hyperedge HE-5 (induced by source paper "rare event detection using disentangled representation learning"):
  with members:
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "multi view data generation without view supervision"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "domain separation networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "generating sentences from a continuous space"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-6 (induced by source paper "detection by localization maintenance free change object detector"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "yolov3 an incremental improvement"
    - "map management for efficient long term visual localization in outdoor environments"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "lightweight unsupervised deep loop closure"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "yolo9000 better faster stronger"

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)