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: "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
  Abstract: In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer the learned knowledge to deal with occlusions. This setting alleviates the difficulty in collecting an exponentially large dataset to cover occlusion patterns and is more essential. In this scenario, the proposal-based deep networks, like RCNN-series, often produce unsatisfactory results, because both the proposal extraction and classification stages may be confused by the irrelevant occluders. To address this, [25] proposed a voting mechanism that combines multiple local visual cues to detect semantic parts. The semantic parts can still be detected even though some visual cues are missing due to occlusions. However, this method is manually-designed, thus is hard to be optimized in an end-to-end manner. #R##N#In this paper, we present DeepVoting, which incorporates the robustness shown by [25] into a deep network, so that the whole pipeline can be jointly optimized. Specifically, it adds two layers after the intermediate features of a deep network, e.g., the pool-4 layer of VGGNet. The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts. We also propose an improved version DeepVoting+ by learning visual cues from context outside objects. In experiments, DeepVoting achieves significantly better performance than several baseline methods, including Faster-RCNN, for semantic part detection under occlusion. In addition, DeepVoting enjoys explainability as the detection results can be diagnosed via looking up the voting cues.

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"
    - "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:
    - "greedy structure learning of hierarchical compositional models"
    - "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 "deep multivariate mixture of gaussians for object detection under occlusion"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "deformable convnets v2 more deformable better results"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-5 (induced by source paper "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "robustness of object recognition under extreme occlusion in humans and computational models"
    - "convolutional neural networks with alternately updated clique"
    - "robust classification with convolutional prototype learning"
    - "prototypical networks for few shot learning"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"
    - "visual concepts and compositional voting"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "robustness of object recognition under extreme occlusion in humans and computational models"):
  with members:
    - "teaching compositionality to cnns"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "semantic part detection via matching learning to generalize to novel viewpoints from limited training data"):
  with members:
    - "acquisition of localization confidence for accurate object detection"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "learning non maximum suppression"
    - "squeeze and excitation networks"
    - "click here human localized keypoints as guidance for viewpoint estimation"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "scnet learning semantic correspondence"

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)