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: "deepproposals hunting objects and actions by cascading deep convolutional layers"
  Abstract: In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informative-ness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because i) it re-uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that our DeepProposals outperform most of the previously proposed object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.

Connected through hyperedge HE-1 (induced by source paper "residual objectness for imbalance reduction"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "zoom out and in network with map attention decision for region proposal and object detection"):
  with members:
    - "learning deep features via congenerous cosine loss for person recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "recurrent scale approximation for object detection in cnn"
    - "stacked hourglass networks for human pose estimation"
    - "scale aware pixelwise object proposal networks"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "zoom out and in network with recursive training for object proposal"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "what makes for effective detection proposals"
    - "multi bias non linear activation in deep neural networks"
    - "craft objects from images"
    - "object proposal evaluation protocol is gameable"
    - "learning to segment object candidates"

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)