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: "teaching compositionality to cnns"
  Abstract: Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions. In this paper, we propose a method for augmenting and training CNNs so that their learned features are compositional. It encourages networks to form representations that disentangle objects from their surroundings and from each other, thereby promoting better generalization. Our method is agnostic to the specific details of the underlying CNN to which it is applied and can in principle be used with any CNN. As we show in our experiments, the learned representations lead to feature activations that are more localized and improve performance over non-compositional baselines in object recognition tasks.

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 "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"
    - "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 "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"

Connected through hyperedge HE-4 (induced by source paper "towards a unified evaluation of explanation methods without ground truth"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluating explainers via perturbation"
    - "can i trust the explainer verifying post hoc explanatory methods"
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "evaluating recurrent neural network explanations"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards robust interpretability with self explaining neural networks"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised compositional featureaggregation for few shot recognition"):
  with members:
    - "measuring compositionality in representation learning"
    - "generalizing from a few examples a survey on few shot learning"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "few shot learning with metric agnostic conditional embeddings"
    - "few shot object detection via feature reweighting"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-6 (induced by source paper "semantically interpretable activation maps what where how explanations within cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gradmask reduce overfitting by regularizing saliency"
    - "interpretability beyond classification output semantic bottleneck networks"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "methods for interpreting and understanding deep neural networks"
    - "semantic bottleneck for computer vision tasks"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "robustness of object recognition under extreme occlusion in humans and computational models"):
  with members:
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "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"

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)