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: "deep regionlets blended representation and deep learning for generic object detection"
  Abstract: In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the advantages of regionlets on modeling object deformation and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select regions from which features can be learned from. The regionlet learning module focuses on local feature selection and transformation to alleviate the effects of appearance variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable soft regionlet selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We present the results of ablation studies and extensive experiments on PASCAL VOC and Microsoft COCO datasets. The proposed algorithm outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.

Connected through hyperedge HE-1 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"

Connected through hyperedge HE-2 (induced by source paper "learning simple thresholded features with sparse support recovery"):
  with members:
    - "cross domain visual recognition via domain adaptive dictionary learning"
    - "a fast and accurate system for face detection identification and verification"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"
    - "optimized projections for compressed sensing via direct mutual coherence minimization"

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)