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: "crf cnn modeling structured information in human pose estimation"
  Abstract: Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model structural information in both output and hidden feature layers in a probabilistic way, and it is applied to human pose estimation. A message passing scheme is proposed, so that in various layers each body joint receives messages from all the others in an efficient way. Such message passing can be implemented with convolution between features maps in the same layer, and it is also integrated with feedforward propagation in neural networks. Finally, a neural network implementation of end-to-end learning CRF-CNN is provided. Its effectiveness is demonstrated through experiments on two benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "reasoning visual dialogs with structural and partial observations"):
  with members:
    - "flipdial a generative model for two way visual dialogue"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning visual question answering by bootstrapping hard attention"
    - "discriminability objective for training descriptive captions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning human object interactions by graph parsing neural networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"

Connected through hyperedge HE-2 (induced by source paper "human pose estimation with spatial contextual information"):
  with members:
    - "path aggregation network for instance segmentation"
    - "situation recognition with graph neural networks"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"

Connected through hyperedge HE-3 (induced by source paper "fishnet a versatile backbone for image region and pixel level prediction"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "convolutional neural networks with alternately updated clique"
    - "i revnet deep invertible networks"
    - "question guided hybrid convolution for visual question answering"
    - "deep layer aggregation"
    - "learning feature pyramids for human pose estimation"
    - "dilated residual networks"
    - "multi scale dense networks for resource efficient image classification"

Connected through hyperedge HE-4 (induced by source paper "crowd counting with deep structured scale integration network"):
  with members:
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "context aware crowd counting"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "attentive crowd flow machines"

Connected through hyperedge HE-5 (induced by source paper "trb a novel triplet representation for understanding 2d human body"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "multi person articulated tracking with spatial and temporal embeddings"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "dense pose transfer"
    - "densepose dense human pose estimation in the wild"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-6 (induced by source paper "anti confusing region aware network for human pose estimation"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "devil in the details towards accurate single and multiple human parsing"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "structured modeling of joint deep feature and prediction refinement for salient object detection"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "reverse attention for salient object detection"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-8 (induced by source paper "dense fusion classmate network for land cover classification"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "stacked u nets a no frills approach to natural image segmentation"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "deep layer aggregation"
    - "an overview of multi task learning in deep neural networks"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

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)