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: "scribblesup scribble supervised convolutional networks for semantic segmentation"
  Abstract: Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, scribbles are very widely used in academic research and commercial software, and are recognized as one of the most user-friendly ways of interacting. In this paper, we propose to use scribbles to annotate images, and develop an algorithm to train convolutional networks for semantic segmentation supervised by scribbles. Our algorithm is based on a graphical model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. We present competitive object semantic segmentation results on the PASCAL VOC dataset by using scribbles as annotations. Scribbles are also favored for annotating stuff (e.g., water, sky, grass) that has no well-defined shape, and our method shows excellent results on the PASCAL-CONTEXT dataset thanks to extra inexpensive scribble annotations. Our scribble annotations on PASCAL VOC are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "image to image translation for domain adaptation"
    - "residual parameter transfer for deep domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised semantic segmentation by iterative affinity learning"):
  with members:
    - "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"
    - "edge preserving and multi scale contextual neural network for salient object detection"
    - "learning affinity via spatial propagation networks"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class"):
  with members:
    - "confidence regularized self training"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic 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"
    - "weakly and semi supervised panoptic segmentation"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised salient object detection via scribble annotations"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "gated crf loss for weakly supervised semantic image segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "weakly supervised salient object detection using image labels"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "train learn expand repeat"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"
    - "development and validation of deep learning algorithms for detection of critical findings in head ct scans"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-8 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "angle closure detection in anterior segment oct based on multilevel deep network"
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"

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)