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 level contextual rnns with attention model for scene labeling"
  Abstract: Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored. To handle this issue, we in this work propose a novel approach for scene labeling by exploring multi-level contextual recurrent neural networks (ML-CRNNs). Specifically, we encode three kinds of contextual cues, i.e., local context, global context and image topic context in structural recurrent neural networks (RNNs) to model long-range local and global dependencies in image. In this way, our method is able to `see' the image in terms of both long-range local and holistic views, and make a more reliable inference for image labeling. Besides, we integrate the proposed contextual RNNs into hierarchical convolutional neural networks (CNNs), and exploit dependence relationships in multiple levels to provide rich spatial and semantic information. Moreover, we novelly adopt an attention model to effectively merge multiple levels and show that it outperforms average- or max-pooling fusion strategies. Extensive experiments demonstrate that the proposed approach achieves new state-of-the-art results on the CamVid, SiftFlow and Stanford-background datasets.

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "feature fusion for online mutual knowledge distillation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi level factorisation net for person re identification"
    - "paraphrasing complex network network compression via factor transfer"
    - "large scale distributed neural network training through online distillation"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a survey of model compression and acceleration for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "feature pyramid and hierarchical boosting network for pavement crack detection"):
  with members:
    - "focal loss for dense object detection"
    - "loss max pooling for semantic image segmentation"
    - "richer convolutional features for edge detection"
    - "training region based object detectors with online hard example mining"
    - "feature pyramid networks for object detection"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "holistically nested edge detection"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi level factorisation net for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "scalable person re identification on supervised smoothed manifold"
    - "deep mutual learning"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-5 (induced by source paper "robust and efficient graph correspondence transfer for person re identification"):
  with members:
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "sanet structure aware network for visual tracking"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "semantic image segmentation via deep parsing network"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-6 (induced by source paper "deep contextual recurrent residual networks for scene labeling"):
  with members:
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "reseg a recurrent neural network for object segmentation"
    - "recurrent models of visual attention"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"
    - "fully convolutional networks for semantic segmentation"
    - "recurrent convolutional neural networks for scene parsing"

Connected through hyperedge HE-7 (induced by source paper "scene labeling using gated recurrent units with explicit long range conditioning"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "learning common and specific features for rgb d semantic segmentation with deconvolutional networks"
    - "multi path feedback recurrent neural network for scene parsing"
    - "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
    - "pixel recurrent neural networks"
    - "semantic image segmentation via deep parsing network"

Connected through hyperedge HE-8 (induced by source paper "sanet structure aware network for visual tracking"):
  with members:
    - "siamese instance search for tracking"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "pixel recurrent neural networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "online tracking by learning discriminative saliency map with convolutional neural network"

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)