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: "incorporating network built in priors in weakly supervised semantic segmentation"
  Abstract: Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract accurate masks from networks pre-trained for the task of object recognition, thus forgoing external objectness modules. We first show how foreground/background masks can be obtained from the activations of higher-level convolutional layers of a network. We then show how to obtain multi-class masks by the fusion of foreground/background ones with information extracted from a weakly-supervised localization network. Our experiments evidence that exploiting these masks in conjunction with a weakly-supervised training loss yields state-of-the-art tag-based weakly-supervised semantic segmentation results.

Connected through hyperedge HE-1 (induced by source paper "restricted deformable convolution based road scene semantic segmentation using surround view cameras"):
  with members:
    - "rovo robust omnidirectional visual odometry for wide baseline wide fov camera systems"
    - "deformable convolutional networks"
    - "dense transformer networks"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "decomposeme simplifying convnets for end to end learning"
    - "revisiting batch normalization for practical domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "fully using classifiers for weakly supervised semantic segmentation with modified cues"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "saliency guided deep network for weakly supervised image segmentation"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "the effect of scene context on weakly supervised semantic segmentation"):
  with members:
    - "context encoding for semantic segmentation"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "learning deconvolution network for semantic segmentation"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-4 (induced by source paper "effective use of synthetic data for urban scene semantic segmentation"):
  with members:
    - "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"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-5 (induced by source paper "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "self explanatory deep salient object detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "deep self taught learning for weakly supervised object localization"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "two phase learning for weakly supervised object localization"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-6 (induced by source paper "generating self guided dense annotations for weakly supervised semantic segmentation"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "context encoding for semantic segmentation"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised learning for tool localization in laparoscopic videos"):
  with members:
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "two phase learning for weakly supervised object localization"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised segment annotation via expectation kernel density estimation"):
  with members:
    - "feature and region selection for visual learning"
    - "weakly supervised deep detection networks"
    - "weakly supervised learning for salient object detection using background images"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "conceptlearner discovering visual concepts from weakly labeled image collections"
    - "rich feature hierarchies for accurate object detection and 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)