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: "recurrent attentional reinforcement learning for multi label image recognition"
  Abstract: Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks. The step of hypothesis regions (region proposals) localization in these existing multi-label image recognition pipelines, however, usually takes redundant computation cost, e.g., generating hundreds of meaningless proposals with non-discriminative information and extracting their features, and the spatial contextual dependency modeling among the localized regions are often ignored or over-simplified. To resolve these issues, this paper proposes a recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions. Besides, our method explicitly models long-term dependencies among these attentional regions that help to capture semantic label co-occurrence and thus facilitate multi-label recognition. Extensive experiments and comparisons on two large-scale benchmarks (i.e., PASCAL VOC and MS-COCO) show that our model achieves superior performance over existing state-of-the-art methods in both performance and efficiency as well as explicitly identifying image-level semantic labels to specific object regions.

Connected through hyperedge HE-1 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "tripping through time efficient localization of activities in videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"

Connected through hyperedge HE-2 (induced by source paper "ai online filters to real world image recognition"):
  with members:
    - "online learning to estimate warfarin dose with contextual linear bandits"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "deep reinforcement learning with double q learning"
    - "active object localization with deep reinforcement learning"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (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"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"

Connected through hyperedge HE-4 (induced by source paper "orderless recurrent models for multi label classification"):
  with members:
    - "elucidating image to set prediction an analysis of models losses and datasets"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "a comprehensive survey of deep learning for image captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-5 (induced by source paper "multi label image recognition with graph convolutional networks"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "chest x rays classification a multi label and fine grained problem"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-6 (induced by source paper "learning category correlations for multi label image recognition with graph networks"):
  with members:
    - "linkage based face clustering via graph convolution network"
    - "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"
    - "videos as space time region graphs"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning spatial regularization with image level supervisions for multi label image classification"

Connected through hyperedge HE-7 (induced by source paper "learning semantic specific graph representation for multi label image recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-8 (induced by source paper "face hallucination by attentive sequence optimization with reinforcement learning"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "zero shot super resolution using deep internal learning"
    - "parallel multiscale autoregressive density estimation"
    - "learning to hallucinate face images via component generation and enhancement"

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)