human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compositional convolutional neural networks a deep architecture with innate robustness to partial occlusion"):
  Abstract: Recent work has shown that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture Compositional Convolutional Neural Network. In particular, we propose to replace the fully connected classification head of a DCNN with a differentiable compositional model. The generative nature of the compositional model enables it to localize occluders and subsequently focus on the non-occluded parts of the object. We conduct classification experiments on artificially occluded images as well as real images of partially occluded objects from the MS-COCO dataset. The results show that DCNNs do not classify occluded objects robustly, even when trained with data that is strongly augmented with partial occlusions. Our proposed model outperforms standard DCNNs by a large margin at classifying partially occluded objects, even when it has not been exposed to occluded objects during training. Additional experiments demonstrate that CompositionalNets can also localize the occluders accurately, despite being trained with class labels only.

Includes members:
  Member "combining compositional models and deep networks for robust object classification under occlusion":
    also connected through other hyperedges:
      (none)
  Member "compositional convolutional networks for robust object classification under occlusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion")
  Member "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion":
    also connected through other hyperedges:
      (none)
  Member "greedy structure learning of hierarchical compositional models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining compositional models and deep networks for robust object classification under occlusion")
  Member "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combining compositional models and deep networks for robust object classification under occlusion")
  Member "visual concepts and compositional voting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference")
  Member "interpretable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional occupancy networks")
  Member "aognets compositional grammatical architectures for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "concept whitening for interpretable image recognition")

Question: Please tell me which class the source paper 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)