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: "improving weakly supervised object localization by micro annotation"
  Abstract: Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a method to overcome these failures by adding a very small amount of model-specific additional annotation. The main idea is to cluster a deep network's mid-level representations and assign object or distractor labels to each cluster. Experiments show substantially improved localization results on the challenging ILSVC2014 dataset for bounding box detection and the PASCAL VOC2012 dataset for semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "mixed supervised object detection with robust objectness transfer"):
  with members:
    - "weakly supervised object localization using things and stuff transfer"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised cascaded convolutional networks"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "r fcn object detection via region based fully convolutional networks"
    - "weakly supervised deep detection networks"

Connected through hyperedge HE-2 (induced by source paper "exploiting saliency for object segmentation from image level labels"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convexification of learning from constraints"
    - "deep contrast learning for salient object detection"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deepsaliency multi task deep neural network model for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "c wsl count guided weakly supervised localization"):
  with members:
    - "focal loss for dense object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"
    - "weakly supervised object localization using things and stuff transfer"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "an analysis of scale invariance in object detection snip"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-4 (induced by source paper "seed expand and constrain three principles for weakly supervised image segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "deconvolutional feature stacking for weakly supervised semantic segmentation"
    - "learning transferrable knowledge for semantic segmentation with deep convolutional neural network"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "learning deep features for discriminative localization"
    - "what s the point semantic segmentation with point supervision"
    - "very deep convolutional networks for large scale image recognition"

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)