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: "deep hierarchical parsing for semantic segmentation"
  Abstract: This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.

Connected through hyperedge HE-1 (induced by source paper "red net a recursive encoder decoder network for edge detection"):
  with members:
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "densely connected convolutional networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"

Connected through hyperedge HE-2 (induced by source paper "recent progress in semantic image segmentation"):
  with members:
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "dilated residual networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "real time facial segmentation and performance capture from rgb input"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "effective use of synthetic data for urban scene semantic segmentation"):
  with members:
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "image to image translation for domain adaptation"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-4 (induced by source paper "incorporating network built in priors in weakly supervised semantic segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using motion cues"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "weakly supervised semantic labelling and instance segmentation"
    - "conditional random fields as recurrent neural networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "learnable histogram statistical context features for deep neural networks"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "see the difference direct pre image reconstruction and pose estimation by differentiating hog"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "human pose estimation with iterative error feedback"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "semantic video segmentation a review on recent approaches"):
  with members:
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "optical flow with semantic segmentation and localized layers"
    - "convolutional random walk networks for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "attention to scale scale aware semantic image segmentation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-7 (induced by source paper "improving the expressiveness of deep learning frameworks with recursion"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep learning with dynamic computation graphs"
    - "deep structured scene parsing by learning with image descriptions"
    - "a fast unified model for parsing and sentence understanding"
    - "theano a python framework for fast computation of mathematical expressions"
    - "hierarchical multiscale recurrent neural networks"
    - "tensorflow a system for large scale machine learning"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-8 (induced by source paper "learning deep representations for scene labeling with semantic context guided supervision"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "region based semantic segmentation with end to end training"
    - "semantic image segmentation via deep parsing network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "learning deconvolution network 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)