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: "how important are deformable parts in the deformable parts model"
  Abstract: The main stated contribution of the Deformable Parts Model (DPM) detector of Felzenszwalb et al. (over the Histogram-of-Oriented-Gradients approach of Dalal and Triggs) is the use of deformable parts. A secondary contribution is the latent discriminative learning. Tertiary is the use of multiple components. A common belief in the vision community (including ours, before this study) is that their ordering of contributions reflects the performance of detector in practice. However, what we have experimentally found is that the ordering of importance might actually be the reverse. First, we show that by increasing the number of components, and switching the initialization step from their aspect-ratio, left-right flipping heuristics to appearance-based clustering, considerable improvement in performance is obtained. But more intriguingly, we show that with these new components, the part deformations can now be completely switched off, yet obtaining results that are almost on par with the original DPM detector. Finally, we also show initial results for using multiple components on a different problem -- scene classification, suggesting that this idea might have wider applications in addition to object detection.

Connected through hyperedge HE-1 (induced by source paper "objects as context for detecting their semantic parts"):
  with members:
    - "learning semantic part based models from google images"
    - "joint multi person pose estimation and semantic part segmentation"
    - "semantic object parsing with graph lstm"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "do semantic parts emerge in convolutional neural networks"
    - "subset feature learning for fine grained category classification"
    - "joint object and part segmentation using deep learned potentials"

Connected through hyperedge HE-2 (induced by source paper "object detection through search with a foveated visual system"):
  with members:
    - "what makes for effective detection proposals"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "subcategory aware convolutional neural networks for object proposals and detection"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "learning to detect vehicles by clustering appearance patterns"
    - "filtered channel features for pedestrian detection"
    - "fast r cnn"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "recognizing fine grained and composite activities using hand centric features and script data"):
  with members:
    - "a dataset for movie description"
    - "coherent multi sentence video description with variable level of detail"
    - "weakly supervised action labeling in videos under ordering constraints"
    - "zero shot learning through cross modal transfer"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "feature weighted linear stacking"

Connected through hyperedge HE-5 (induced by source paper "fast detection of multiple objects in traffic scenes with a common detection framework"):
  with members:
    - "learning to detect vehicles by clustering appearance patterns"
    - "strengthening the effectiveness of pedestrian detection with spatially pooled features"
    - "asymmetric pruning for learning cascade detectors"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "visualizing object detection features"):
  with members:
    - "image reconstruction from bag of visual words"
    - "understanding deep image representations by inverting them"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning visual biases from human imagination"
    - "understanding image representations by measuring their equivariance and equivalence"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-7 (induced by source paper "learning to detect vehicles by clustering appearance patterns"):
  with members:
    - "strengthening the effectiveness of pedestrian detection with spatially pooled features"
    - "caffe convolutional architecture for fast feature embedding"
    - "deformable part models are convolutional neural networks"
    - "aggregate channel features for multi view face detection"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "learning descriptors for object recognition and 3d pose estimation"):
  with members:
    - "learning fine grained image similarity with deep ranking"
    - "fast exact search in hamming space with multi index hashing"

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)