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: "decoupled deep neural network for semi supervised semantic segmentation"
  Abstract: We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. Contrary to existing approaches posing semantic segmentation as a single task of region-based classification, our algorithm decouples classification and segmentation, and learns a separate network for each task. In this architecture, labels associated with an image are identified by classification network, and binary segmentation is subsequently performed for each identified label in segmentation network. The decoupled architecture enables us to learn classification and segmentation networks separately based on the training data with image-level and pixel-wise class labels, respectively. It facilitates to reduce search space for segmentation effectively by exploiting class-specific activation maps obtained from bridging layers. Our algorithm shows outstanding performance compared to other semi-supervised approaches even with much less training images with strong annotations in PASCAL VOC dataset.

Connected through hyperedge HE-1 (induced by source paper "grid saliency for context explanations of semantic segmentation"):
  with members:
    - "interpreting adversarial examples with attributes"
    - "fast scnn fast semantic segmentation network"
    - "on physical adversarial patches for object detection"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "context proposals for saliency detection"
    - "guided upsampling network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "from handcrafted to deep local features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-3 (induced by source paper "semi supervised semantic segmentation with high and low level consistency"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "context encoding for semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-4 (induced by source paper "leveraging pretrained image classifiers for language based segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "rtseg real time semantic segmentation comparative study"
    - "taskonomy disentangling task transfer learning"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "object level representation learning for few shot image classification"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"

Connected through hyperedge HE-5 (induced by source paper "budget aware semi supervised semantic and instance segmentation"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning to segment via cut and paste"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "pseudo mask augmented object detection"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "the imaterialist fashion attribute dataset"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "webvision database visual learning and understanding from web data"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-7 (induced by source paper "transferable semi supervised 3d object detection from rgb d data"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "weakly supervised instance segmentation using class peak response"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "weakly and semi supervised panoptic segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "an implementation of faster rcnn with study for region sampling"

Connected through hyperedge HE-8 (induced by source paper "certifiably robust interpretation in deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "towards robust locally linear deep networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "generalizing multistain immunohistochemistry tissue segmentation using one shot color deconvolution deep neural networks"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)