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 a hierarchical compositional shape vocabulary for multi class object representation"
  Abstract: Hierarchies allow feature sharing between objects at multiple levels of representation, can code exponential variability in a very compact way and enable fast inference. This makes them potentially suitable for learning and recognizing a higher number of object classes. However, the success of the hierarchical approaches so far has been hindered by the use of hand-crafted features or predetermined grouping rules. This paper presents a novel framework for learning a hierarchical compositional shape vocabulary for representing multiple object classes. The approach takes simple contour fragments and learns their frequent spatial configurations. These are recursively combined into increasingly more complex and class-specific shape compositions, each exerting a high degree of shape variability. At the top-level of the vocabulary, the compositions are sufficiently large and complex to represent the whole shapes of the objects. We learn the vocabulary layer after layer, by gradually increasing the size of the window of analysis and reducing the spatial resolution at which the shape configurations are learned. The lower layers are learned jointly on images of all classes, whereas the higher layers of the vocabulary are learned incrementally, by presenting the algorithm with one object class after another. The experimental results show that the learned multi-class object representation scales favorably with the number of object classes and achieves a state-of-the-art detection performance at both, faster inference as well as shorter training times.

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"
    - "greedy structure learning of hierarchical compositional models"
    - "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"

Connected through hyperedge HE-2 (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-3 (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"
    - "greedy structure learning of hierarchical compositional models"
    - "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"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "greedy structure learning of hierarchical compositional models"):
  with members:
    - "deep nets what have they ever done for vision"

Connected through hyperedge HE-5 (induced by source paper "scene grammars factor graphs and belief propagation"):
  with members:
    - "multiscale fields of patterns"
    - "loopy belief propagation for approximate inference an empirical study"
    - "fast exact inference for recursive cardinality models"

Connected through hyperedge HE-6 (induced by source paper "a framework for symmetric part detection in cluttered scenes"):
  with members:
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)