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: "order free rnn with visual attention for multi label classification"
  Abstract: In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.

Connected through hyperedge HE-1 (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"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "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-2 (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"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deep imbalanced attribute classification using visual attention aggregation"):
  with members:
    - "deep visual attention prediction"
    - "harmonious attention network for person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "focal loss for dense object detection"
    - "curriculum learning for multi task classification of visual attributes"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-4 (induced by source paper "multi label image classification via knowledge distillation from weakly supervised detection"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "kill two birds with one stone weakly supervised neural network for image annotation and tag refinement"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "learning deep latent spaces for multi label classification"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "automatic image annotation via label transfer in the semantic space"
    - "deep patch learning for weakly supervised object classification and discovery"

Connected through hyperedge HE-5 (induced by source paper "attentional multilabel learning over graphs a message passing approach"):
  with members:
    - "graph memory networks for molecular activity prediction"
    - "junction tree variational autoencoder for molecular graph generation"
    - "network representation learning a survey"
    - "representation learning on graphs methods and applications"
    - "graph convolutional networks for classification with a structured label space"
    - "neural message passing for quantum chemistry"
    - "hcp a flexible cnn framework for multi label image classification"
    - "column networks for collective classification"

Connected through hyperedge HE-6 (induced by source paper "adversarial learning of label dependency a novel framework for multi class classification"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "sgm sequence generation model for multi label classification"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "improved training of wasserstein gans"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "object detection from video tubelets with convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning with a rethinking structure for multi label classification"):
  with members:
    - "cnn rnn a unified framework for multi label image classification"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep residual learning for image recognition"
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)