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: "learning sampling distributions for efficient object detection"
  Abstract: Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi  et al. , is an algorithm of fast and accurate object detection. By sampling particle windows (PWs) from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and the number of PWs in each stage. Moreover, it has to generate too many PWs in the initialization step and it unnecessarily regenerates too many PWs around object-like regions. In this paper, we attempt to solve the problems of MPW. An important fact we used is that there is a large probability for a randomly generated PW not to contain the object because the object is a sparse event relative to the huge number of candidate windows. Therefore, we design a PD so as to efficiently reject the huge number of nonobject windows. Specifically, we propose the concepts of rejection, acceptance, and ambiguity windows and regions. Then, the concepts are used to form and update a dented uniform distribution and a dented Gaussian distribution. This contrasts to MPW which utilizes only on region of support. The PD of MPW is acceptance-oriented whereas the PD of our method (called iPW) is rejection-oriented. Experimental results on human and face detection demonstrate the efficiency and the effectiveness of the iPW algorithm. The source code is publicly accessible.

Connected through hyperedge HE-1 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos"):
  with members:
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "region proposal by guided anchoring"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "towards high performance video object detection for mobiles"

Connected through hyperedge HE-2 (induced by source paper "object discovery via cohesion measurement"):
  with members:
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "the secrets of salient object segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning multilayer channel features for pedestrian detection"):
  with members:
    - "cascade learning by optimally partitioning"
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "how far are we from solving pedestrian detection"
    - "scale aware pixelwise object proposal networks"
    - "convolution in convolution for network in network"
    - "scale aware fast r cnn for pedestrian detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "filtered channel features for pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "simultaneously learning neighborship and projection matrix for supervised dimensionality reduction"):
  with members:
    - "vectorial dimension reduction for tensors based on bayesian inference"
    - "cascade learning by optimally partitioning"
    - "cascaded subpatch networks for effective cnns"
    - "convolution in convolution for network in network"
    - "deep linear discriminant analysis"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"

Connected through hyperedge HE-5 (induced by source paper "cascade learning by optimally partitioning"):
  with members:
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "ten years of pedestrian detection what have we learned"
    - "training effective node classifiers for cascade classification"

Connected through hyperedge HE-6 (induced by source paper "cascaded subpatch networks for effective cnns"):
  with members:
    - "convolution in convolution for network in network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training very deep networks"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "fitnets hints for thin deep nets"

Connected through hyperedge HE-7 (induced by source paper "convolution in convolution for network in network"):
  with members:
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "cascaded subpatch networks for effective cnns"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "pcanet a simple deep learning baseline for image classification"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "pedestrian detection inspired by appearance constancy and shape symmetry"):
  with members:
    - "learning multilayer channel features for pedestrian detection"
    - "how far are we from solving pedestrian detection"
    - "scale aware fast r cnn for pedestrian detection"
    - "filtered channel features for pedestrian detection"
    - "exploring human vision driven features for pedestrian detection"
    - "convolutional channel features"
    - "learning complexity aware cascades for deep pedestrian detection"
    - "taking a deeper look at pedestrians"

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)