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: "attention based pyramid aggregation network for visual place recognition"
  Abstract: Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.

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"
    - "deep attentional structured representation learning for visual 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 "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"
    - "deep attentional structured representation learning for visual 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-3 (induced by source paper "accurate visual localization for automotive applications"):
  with members:
    - "deep triplet quantization"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "fine tuning cnn image retrieval with no human annotation"
    - "super convergence very fast training of residual networks using large learning rates"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-4 (induced by source paper "place clustering based feature recombination for visual place recognition"):
  with members:
    - "deep attentional structured representation learning for visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-5 (induced by source paper "semantically aware attentive neural embeddings for image based visual localization"):
  with members:
    - "deep audio visual speech recognition"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "night to day image translation for retrieval based localization"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "long term visual localization using semantically segmented images"
    - "cbam convolutional block attention module"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (induced by source paper "learnable pooling methods for video classification"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "attention is all you need"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "learnable pooling with context gating for video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "neural photo editing with introspective adversarial networks"

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)