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: "boosting convolutional features for robust object proposals"
  Abstract: Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion. Modern detection algorithms like Regions with CNNs (Girshick et al., 2014) rely on Selective Search (Uijlings et al., 2013) to propose regions which with high probability represent objects, where in turn CNNs are deployed for classification. Selective Search represents a family of sophisticated algorithms that are engineered with multiple segmentation, appearance and saliency cues, typically coming with a significant run-time overhead. Furthermore, (Hosang et al., 2014) have shown that most methods suffer from low reproducibility due to unstable superpixels, even for slight image perturbations. Although CNNs are subsequently used for classification in top-performing object-detection pipelines, current proposal methods are agnostic to how these models parse objects and their rich learned representations. As a result they may propose regions which may not resemble high-level objects or totally miss some of them. To overcome these drawbacks we propose a boosting approach which directly takes advantage of hierarchical CNN features for detecting regions of interest fast. We demonstrate its performance on ImageNet 2013 detection benchmark and compare it with state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "deep metric learning with bier boosting independent embeddings robustly"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "adversarial discriminative domain adaptation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "smart mining for deep metric learning"
    - "siamese instance search for tracking"
    - "deep metric learning via facility location"

Connected through hyperedge HE-2 (induced by source paper "crossbar net a novel convolutional network for kidney tumor segmentation in ct images"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"
    - "matconvnet convolutional neural networks for matlab"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "lecture video indexing using boosted margin maximizing neural networks"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "cgmos certainty guided minority oversampling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-4 (induced by source paper "convolutional channel features"):
  with members:
    - "multi view face detection using deep convolutional neural networks"
    - "filtered channel features for pedestrian detection"
    - "fast r cnn"
    - "taking a deeper look at pedestrians"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "untangling local and global deformations in deep convolutional networks for image classification and sliding window detection"
    - "densenet implementing efficient convnet descriptor pyramids"

Connected through hyperedge HE-5 (induced by source paper "cascaded sparse spatial bins for efficient and effective generic object detection"):
  with members:
    - "what makes for effective detection proposals"
    - "object proposal evaluation protocol is gameable"
    - "understanding the fisher vector a multimodal part model"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "scalable high quality object detection"
    - "how good are detection proposals really"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)