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: "learning structured inference neural networks with label relations"
  Abstract: Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with fine-grained labels that describe major components, coarse-grained labels that depict high level abstraction or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.

Connected through hyperedge HE-1 (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"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning spatial regularization with image level supervisions for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"):
  with members:
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "imbalanced deep learning by minority class incremental rectification"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "putting visual object recognition in context"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "lift the flap context reasoning using object centered graphs"
    - "iterative visual reasoning beyond convolutions"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning what and where to attend"
    - "learning scene gist with convolutional neural networks to improve object recognition"

Connected through hyperedge HE-4 (induced by source paper "uncertainty modeling of contextual connection between tracklets for unconstrained video based face recognition"):
  with members:
    - "recurrent embedding aggregation network for video face recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "person search in videos with one portrait through visual and temporal links"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "an automatic system for unconstrained video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-5 (induced by source paper "lift the flap what where and when for context reasoning"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "finding any waldo with zero shot invariant and efficient visual search"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning scene gist with convolutional neural networks to improve object recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "scene graph generation by iterative message passing"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "vital a visual interpretation on text with adversarial learning for image labeling"):
  with members:
    - "unsupervised textual grounding linking words to image concepts"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "conditional image text embedding networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "multichannel variable size convolution for sentence classification"
    - "learning what and where to draw"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-7 (induced by source paper "lift the flap context reasoning using object centered graphs"):
  with members:
    - "learning scene gist with convolutional neural networks to improve object recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "scene graph generation by iterative message passing"
    - "deep reinforcement learning for robotic manipulation the state of the art"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "the more you know using knowledge graphs for image classification"
    - "interaction networks for learning about objects relations and physics"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"

Connected through hyperedge HE-8 (induced by source paper "a cnn rnn framework for image annotation from visual cues and social network metadata"):
  with members:
    - "modeling visual context is key to augmenting object detection datasets"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "automatic image annotation via label transfer in the semantic space"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"
    - "webly supervised learning of convolutional networks"
    - "improving image classification with location context"

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)