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: "region based semantic segmentation with end to end training"
  Abstract: We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class. Our method combines the advantages of the two main competing paradigms. Methods based on region classification offer proper spatial support for appearance measurements, but typically operate in two separate stages, none of which targets pixel labeling performance at the end of the pipeline. More recent fully convolutional methods are capable of end-to-end training for the final pixel labeling, but resort to fixed patches as spatial support. We show how to modify modern region-based approaches to enable end-to-end training for semantic segmentation. This is achieved via a differentiable region-to-pixel layer and a differentiable free-form Region-of-Interest pooling layer. Our method improves the state-of-the-art in terms of class-average accuracy with 64.0% on SIFT Flow and 49.9% on PASCAL Context, and is particularly accurate at object boundaries.

Connected through hyperedge HE-1 (induced by source paper "object contextual representations for semantic segmentation"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "acfnet attentional class feature network for semantic segmentation"
    - "dual graph convolutional network for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "global aggregation then local distribution in fully convolutional networks"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"

Connected through hyperedge HE-2 (induced by source paper "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"):
  with members:
    - "y net joint segmentation and classification for diagnosis of breast biopsy images"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dilated residual networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning to segment breast biopsy whole slide images"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "interactive full image segmentation by considering all regions jointly"):
  with members:
    - "panoptic segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "guide me interacting with deep networks"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-4 (induced by source paper "vision based navigation of autonomous vehicle in roadway environments with unexpected hazards"):
  with members:
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "on the exploration of convolutional fusion networks for visual recognition"
    - "lost and found detecting small road hazards for self driving vehicles"
    - "end to end learning for self driving cars"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep dual pyramid network for barcode segmentation using barcode 30k database"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-6 (induced by source paper "actor action semantic segmentation with region masks"):
  with members:
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "learning video object segmentation from static images"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "learning to segment object candidates"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-7 (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"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "superpixel based semantic segmentation trained by statistical process control"):
  with members:
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a multipath network for object detection"
    - "improving fully convolution network for semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "parsenet looking wider to see better"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)