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: "deep attentional structured representation learning for visual recognition"
  Abstract: Structured representations, such as Bags of Words, VLAD and Fisher Vectors, have proven highly effective to tackle complex visual recognition tasks. As such, they have recently been incorporated into deep architectures. However, while effective, the resulting deep structured representation learning strategies typically aggregate local features from the entire image, ignoring the fact that, in complex recognition tasks, some regions provide much more discriminative information than others. #R##N#In this paper, we introduce an attentional structured representation learning framework that incorporates an image-specific attention mechanism within the aggregation process. Our framework learns to predict jointly the image class label and an attention map in an end-to-end fashion and without any other supervision than the target label. As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "psdf fusion probabilistic signed distance function for on the fly 3d data fusion and scene reconstruction"

Connected through hyperedge HE-2 (induced by source paper "interpretable bow networks for adversarial example detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learnable histogram statistical context features for deep neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "improved training of wasserstein gans"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "attention aware age agnostic visual place recognition"):
  with members:
    - "deep cnn based multi task learning for open set recognition"
    - "discriminative label consistent domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "attention based pyramid aggregation network for visual place recognition"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "attention based ensemble for deep metric learning"
    - "panorama to panorama matching for location recognition"

Connected through hyperedge HE-4 (induced by source paper "deep discriminative representation learning with attention map for scene classification"):
  with members:
    - "on the convergence of adam and beyond"
    - "weakly supervised instance segmentation using class peak response"
    - "bam bottleneck attention module"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "attention is all you need"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"

Connected through hyperedge HE-5 (induced by source paper "place clustering based feature recombination for visual place recognition"):
  with members:
    - "attention based pyramid aggregation network for visual place recognition"
    - "netvlad cnn architecture for weakly supervised place 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)