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: "annotation order matters recurrent image annotator for arbitrary length image tagging"
  Abstract: Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.

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 "boosted gan with semantically interpretable information for image inpainting"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "rethinking atrous convolution for semantic image segmentation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "hcp a flexible cnn framework for multi label image classification"
    - "high resolution image inpainting using multi scale neural patch synthesis"

Connected through hyperedge HE-3 (induced by source paper "adapting rnn sequence prediction model to multi label set prediction"):
  with members:
    - "sgm sequence generation model for multi label classification"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "cnn rnn a unified framework for multi label image classification"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "zero shot learning with sparse attribute propagation"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot kernel learning"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "learning from noisy web data with category level supervision"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "transductive zero shot learning with a self training dictionary approach"

Connected through hyperedge HE-5 (induced by source paper "wavelet convolutional neural networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "wide residual networks"
    - "compact bilinear pooling"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "tagging like humans diverse and distinct image annotation"):
  with members:
    - "multi label learning with missing labels using mixed dependency graphs"
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "improving pairwise ranking for multi label image classification"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-7 (induced by source paper "multi modal multi scale deep learning for large scale image annotation"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning from weak and noisy labels for semantic segmentation"
    - "tweet2vec character based distributed representations for social media"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "order free rnn with visual attention for multi label classification"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "hcp a flexible cnn framework for multi label image classification"
    - "object detection from video tubelets with convolutional neural networks"
    - "semantic regularisation for recurrent image annotation"
    - "learning structured inference neural networks with label relations"
    - "show attend and tell neural image caption generation with visual attention"
    - "compositional distributional semantics with long short term memory"
    - "deep residual learning for 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)