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: "multi label image recognition by recurrently discovering attentional regions"
  Abstract: This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding. Current solutions for this task usually rely on an extra step of extracting hypothesis regions (i.e., region proposals), resulting in redundant computation and sub-optimal performance. In this work, we achieve the interpretable and contextualized multi-label image classification by developing a recurrent memorized-attention module. This module consists of two alternately performed components: i) a spatial transformer layer to locate attentional regions from the convolutional feature maps in a region-proposal-free way and ii) an LSTM (Long-Short Term Memory) sub-network to sequentially predict semantic labeling scores on the located regions while capturing the global dependencies of these regions. The LSTM also output the parameters for computing the spatial transformer. On large-scale benchmarks of multi-label image classification (e.g., MS-COCO and PASCAL VOC 07), our approach demonstrates superior performances over other existing state-of-the-arts in both accuracy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised complementary parts models for fine grained image classification from the bottom up"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "multi attention multi class constraint for fine grained image recognition"
    - "features for multi target multi camera tracking and re identification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-4 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "multi label learning with missing labels using mixed dependency graphs"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "learning deep latent spaces for multi label classification"
    - "recurrent attentional reinforcement learning for multi label image recognition"

Connected through hyperedge HE-5 (induced by source paper "exploring overall contextual information for image captioning in human like cognitive style"):
  with members:
    - "knowledge embedded representation learning for fine grained image recognition"
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "rethinking the form of latent states in image captioning"
    - "learning to parse and translate improves neural machine translation"
    - "convolutional image captioning"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "an analysis of scale invariance in object detection snip"

Connected through hyperedge HE-6 (induced by source paper "cross modality attention with semantic graph embedding for multi label classification"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "approach for video classification with multi label on youtube 8m dataset"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label zero shot learning with structured knowledge graphs"
    - "order free rnn with visual attention for multi label classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "skip rnn learning to skip state updates in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "multi label classification with label graph superimposing"):
  with members:
    - "centrality graph convolutional networks for skeleton based action recognition"
    - "multi label image recognition with graph convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "optimized skeleton based action recognition via sparsified graph regression"

Connected through hyperedge HE-8 (induced by source paper "g softmax improving intra class compactness and inter class separability of features"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "order free rnn with visual attention for multi label classification"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "dilated residual networks"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "densely connected convolutional networks"
    - "cnn rnn a unified framework for multi label image classification"

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)