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: "hi fi hierarchical feature integration for skeleton detection"
  Abstract: In natural images, skeleton scales (thickness) may significantly vary among objects and object parts. Thus, robust skeleton detection requires more powerful multi-scale feature integration ability than other vision tasks. In this paper, we present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the object skeleton detection problem. CNNs can capture high-level semantics from deeper layers as well as low-level details from shallower layers. #R##N#By hierarchically integrating convolutional features with bi-direction guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses strong ability to capture both rich object context and high-resolution details. Experimental results on several benchmarks show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on skeleton detection. Our method also refreshes the state-of-the-art performance of boundary detection task.

Connected through hyperedge HE-1 (induced by source paper "res2net a new multi scale backbone architecture"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "high frequency residual learning for multi scale image classification"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "p2p net bidirectional point displacement net for shape transform"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deepflux for skeletons in the wild"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "linear span network for object skeleton detection"
    - "amat medial axis transform for natural images"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-3 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "deepflux for skeletons in the wild"):
  with members:
    - "linear span network for object skeleton detection"
    - "amat medial axis transform for natural images"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "convolutional oriented boundaries from image segmentation to high level tasks"

Connected through hyperedge HE-6 (induced by source paper "optimizing the f measure for threshold free salient object detection"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "egnet edge guidance network for salient object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "richer convolutional features for edge detection"

Connected through hyperedge HE-7 (induced by source paper "richer convolutional features for edge detection"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "webseg learning semantic segmentation from web searches"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "casenet deep category aware semantic edge detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder 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)