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: "greedy structure learning of hierarchical compositional models"
  Abstract: In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter. Existing approaches to this problem are limited by making strong a-priori assumptions about the object's geometric structure and require segmented training data for learning. In this paper, we propose a novel framework for learning hierarchical compositional models (HCMs) which do not suffer from the mentioned limitations. We present a generalized formulation of HCMs and describe a greedy structure learning framework that consists of two phases: Bottom-up part learning and top-down model composition. Our framework integrates the foreground-background segmentation problem into the structure learning task via a background model. As a result, we can jointly optimize for the number of layers in the hierarchy, the number of parts per layer and a foreground-background segmentation based on class labels only. We show that the learned HCMs are semantically meaningful and achieve competitive results when compared to other generative object models at object classification on a standard transfer learning dataset.

Connected through hyperedge HE-1 (induced by source paper "compositional convolutional neural networks a deep architecture with innate robustness to partial occlusion"):
  with members:
    - "combining compositional models and deep networks for robust object classification under occlusion"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "visual concepts and compositional voting"
    - "interpretable convolutional neural networks"
    - "aognets compositional grammatical architectures for deep learning"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-2 (induced by source paper "combining compositional models and deep networks for robust object classification under occlusion"):
  with members:
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "interpretable convolutional neural networks"
    - "teaching compositionality to cnns"
    - "towards deep compositional networks"
    - "deep residual learning for image recognition"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-3 (induced by source paper "object as hotspots an anchor free 3d object detection approach via firing of hotspots"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "iou loss for 2d 3d object detection"
    - "point voxel cnn for efficient 3d deep learning"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"

Connected through hyperedge HE-4 (induced by source paper "compositional convolutional networks for robust object classification under occlusion"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "interpretable convolutional neural networks"
    - "improved regularization of convolutional neural networks with cutout"
    - "teaching compositionality to cnns"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
    - "learning a hierarchical compositional shape vocabulary for multi class object representation"
    - "very deep convolutional networks for large scale image 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)