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: "recent advances in object detection in the age of deep convolutional neural networks"
  Abstract: Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "gabor convolutional networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-2 (induced by source paper "mixup regularization for region proposal based object detectors"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "towards adversarially robust object detection"
    - "bag of freebies for training object detection neural networks"
    - "learning to generate synthetic data via compositing"
    - "on physical adversarial patches for object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "regularization for deep learning a taxonomy"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "fsd feature skyscraper detector for stem end and blossom end of navel orange"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "convolutional neural networks with alternately updated clique"
    - "deep learning for generic object detection a survey"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "fssd feature fusion single shot multibox detector"
    - "self normalizing neural networks"

Connected through hyperedge HE-4 (induced by source paper "fast object detection in compressed jpeg images"):
  with members:
    - "fast object detection in compressed video"
    - "yolov3 an incremental improvement"
    - "deep residual learning in the jpeg transform domain"
    - "harmonic networks integrating spectral information into cnns"
    - "towards image understanding from deep compression without decoding"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "fssd feature fusion single shot multibox detector"
    - "compressed video action 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)