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 classification with regional latent semantic dependencies"
  Abstract: Deep convolution neural networks (CNN) have demonstrated advanced performance on single-label image classification, and various progress also have been made to apply CNN methods on multi-label image classification, which requires to annotate objects, attributes, scene categories etc. in a single shot. Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at global level, largely improving the labeling capacity. However, predicting small objects and visual concepts is still challenging due to the limited discrimination of the global visual features. In this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to address this problem. The utilized model includes a fully convolutional localization architecture to localize the regions that may contain multiple highly-dependent labels. The localized regions are further sent to the recurrent neural networks (RNN) to characterize the latent semantic dependencies at the regional level. Experimental results on several benchmark datasets show that our proposed model achieves the best performance compared to the state-of-the-art models, especially for predicting small objects occurred in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using bounding box coordinates during training, the experimental results also show that our RLSD can approach the upper bound without using the bounding-box annotations, which is more realistic in the real world.

Connected through hyperedge HE-1 (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"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"):
  with members:
    - "automated curriculum learning for neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "deep self taught learning for weakly supervised object localization"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "multiple instance detection network with online instance classifier refinement"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "multi label image recognition by recurrently discovering attentional regions"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "recurrent attentional networks for saliency detection"
    - "spatial transformer networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "exploit bounding box annotations for multi label object recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "improving classification by improving labelling introducing probabilistic multi label object interaction recognition"):
  with members:
    - "sembed semantic embedding of egocentric action videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "going deeper into first person activity recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "compact cnn for indexing egocentric videos"
    - "language models for image captioning the quirks and what works"
    - "unsupervised learning from narrated instruction videos"
    - "deep compositional captioning describing novel object categories without paired training data"

Connected through hyperedge HE-5 (induced by source paper "recurrent attentional reinforcement learning for multi label image recognition"):
  with members:
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "dynamic memory networks for visual and textual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "exploit bounding box annotations for multi label object recognition"
    - "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)