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: "towards deep compositional networks"
  Abstract: Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality discriminative feature learning, the downside of CNNs is the lack of explicit structure in features, which often leads to overfitting, absence of reconstruction from partial observations and limited generative abilities. Explicit structure is inherent in hierarchical compositional models, however, these lack the ability to optimize a well-defined cost function. We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function. Our experiments on two datasets show that the proposed compositional model performs on a par with standard CNNs on discriminative tasks, while, due to explicit modeling of the structure in the feature units, affording a straight-forward visualization of parts and faster inference due to separability of the units. Actions

Connected through hyperedge HE-1 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatially adaptive filter units for deep neural networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-2 (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-3 (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"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "interpretable convolutional neural networks"
    - "teaching compositionality to cnns"
    - "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-4 (induced by source paper "teaching compositionality to cnns"):
  with members:
    - "object centric representation learning from unlabeled videos"
    - "attend infer repeat fast scene understanding with generative models"
    - "the curious robot learning visual representations via physical interactions"
    - "deep learning in neural networks"
    - "learning to see by moving"
    - "learning and or models to represent context and occlusion for car detection and viewpoint estimation"
    - "slow and steady feature analysis higher order temporal coherence in video"
    - "exploring person context and local scene context for object detection"

Connected through hyperedge HE-5 (induced by source paper "spatially adaptive filter units for deep neural networks"):
  with members:
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "structured receptive fields in cnns"
    - "aggregated residual transformations for deep neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)