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: "joint calibration for semantic segmentation"
  Abstract: Semantic segmentation is the task of assigning a class-label to each pixel in an image. We propose a region-based semantic segmentation framework which handles both full and weak supervision, and addresses three common problems: (1) Objects occur at multiple scales and therefore we should use regions at multiple scales. However, these regions are overlapping which creates conflicting class predictions at the pixel-level. (2) Class frequencies are highly imbalanced in realistic datasets. (3) Each pixel can only be assigned to a single class, which creates competition between classes. We address all three problems with a joint calibration method which optimizes a multi-class loss defined over the final pixel-level output labeling, as opposed to simply region classification. Our method outperforms the state-of-the-art on the popular SIFT Flow [18] dataset in both the fully and weakly supervised setting by a considerably margin (+6% and +10%, respectively).

Connected through hyperedge HE-1 (induced by source paper "metafusion controlled false negative reduction of minority classes in semantic segmentation"):
  with members:
    - "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"
    - "time dynamic estimates of the reliability of deep semantic segmentation networks"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep imbalanced learning for face recognition and attribute prediction"):
  with members:
    - "learning towards minimum hyperspherical energy"
    - "learning to reweight examples for robust deep learning"
    - "feature transfer learning for deep face recognition with under represented data"
    - "sphereface deep hypersphere embedding for face recognition"
    - "improving facial attribute prediction using semantic segmentation"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "one shot face recognition by promoting underrepresented classes"
    - "class rectification hard mining for imbalanced deep learning"

Connected through hyperedge HE-3 (induced by source paper "a network structure to explicitly reduce confusion errors in semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "the devil is in the decoder classification regression and gans"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "high performance semantic segmentation using very deep fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "loss max pooling for semantic image segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pixelnet towards a general pixel level architecture"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-5 (induced by source paper "learning from weak and noisy labels for semantic segmentation"):
  with members:
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"
    - "fully convolutional multi class multiple instance learning"
    - "from image level to pixel level labeling with convolutional networks"
    - "fully convolutional networks for semantic segmentation"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "do semantic parts emerge in convolutional neural networks"):
  with members:
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "fast r cnn"
    - "understanding deep image representations by inverting them"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-7 (induced by source paper "region based semantic segmentation with end to end training"):
  with members:
    - "deep hierarchical parsing for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "image parsing with a wide range of classes and scene level context"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "very deep convolutional networks for large scale 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)