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: "visual concept recognition and localization via iterative introspection"
  Abstract: Convolutional neural networks have been shown to develop internal representations, which correspond closely to semantically meaningful objects and parts, although trained solely on class labels. Class Activation Mapping (CAM) is a recent method that makes it possible to easily highlight the image regions contributing to a network's classification decision. We build upon these two developments to enable a network to re-examine informative image regions, which we term introspection. We propose a weakly-supervised iterative scheme, which shifts its center of attention to increasingly discriminative regions as it progresses, by alternating stages of classification and introspection. We evaluate our method and show its effectiveness over a range of several datasets, where we obtain competitive or state-of-the-art results: on Stanford-40 Actions, we set a new state-of the art of 81.74%. On FGVC-Aircraft and the Stanford Dogs dataset, we show consistent improvements over baselines, some of which include significantly more supervision.

Connected through hyperedge HE-1 (induced by source paper "hr cam precise localization of pathology using multi level learning in cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "respond cam analyzing deep models for 3d imaging data by visualizations"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "multi attention multi class constraint for fine grained image recognition"):
  with members:
    - "generalized orderless pooling performs implicit salient matching"
    - "deep metric learning with angular loss"
    - "squeeze and excitation networks"
    - "diversified visual attention networks for fine grained object classification"
    - "residual attention network for image classification"
    - "the inaturalist species classification and detection dataset"
    - "dynamic computational time for visual attention"
    - "soft proposal networks for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "learning to zoom a saliency based sampling layer for neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deformable convolutional networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "dense transformer networks"
    - "the inaturalist species classification and detection dataset"
    - "dynamic computational time for visual attention"
    - "autoscaler scale attention networks for visual correspondence"
    - "eye tracking for everyone"

Connected through hyperedge HE-4 (induced by source paper "generalized orderless pooling performs implicit salient matching"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "weakly supervised fine grained categorization with part based image representation"
    - "interferences in match kernels"
    - "understanding neural networks through deep visualization"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "active object localization in visual situations"):
  with members:
    - "deep feature based contextual model for object detection"
    - "recovering hard to find object instances by sampling context based object proposals"
    - "object scene convolutional neural networks for event recognition in images"
    - "searching for objects using structure in indoor scenes"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "visual semantic role labeling"

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)