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: "beyond the pixel wise loss for topology aware delineation"
  Abstract: Delineation of curvilinear structures is an important problem in Computer Vision with multiple practical applications. With the advent of Deep Learning, many current approaches on automatic delineation have focused on finding more powerful deep architectures, but have continued using the habitual pixel-wise losses such as binary cross-entropy. In this paper we claim that pixel-wise losses alone are unsuitable for this problem because of their inability to reflect the topological impact of mistakes in the final prediction. We propose a new loss term that is aware of the higher-order topological features of linear structures. We also introduce a refinement pipeline that iteratively applies the same model over the previous delineation to refine the predictions at each step while keeping the number of parameters and the complexity of the model constant. #R##N#When combined with the standard pixel-wise loss, both our new loss term and our iterative refinement boost the quality of the predicted delineations, in some cases almost doubling the accuracy as compared to the same classifier trained with the binary cross-entropy alone. We show that our approach outperforms state-of-the-art methods on a wide range of data, from microscopy to aerial images.

Connected through hyperedge HE-1 (induced by source paper "improving image autoencoder embeddings with perceptual loss"):
  with members:
    - "autoencoder based articulatory to acoustic mapping for ultrasound silent speech interfaces"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "recurrent world models facilitate policy evolution"
    - "a pitfall of unsupervised pre training"
    - "perceptual generative adversarial networks for small object detection"
    - "perceptual losses for real time style transfer and super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-2 (induced by source paper "pretraining image encoders without reconstruction via feature prediction loss"):
  with members:
    - "improving image autoencoder embeddings with perceptual loss"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual losses for real time style transfer and super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "on the quantitative analysis of decoder based generative models"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-3 (induced by source paper "cldice a topology preserving loss function for tubular structure segmentation"):
  with members:
    - "shape aware complementary task learning for multi organ segmentation"
    - "deepvesselnet vessel segmentation centerline prediction and bifurcation detection in 3 d angiographic volumes"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "efficient algorithms for moral lineage tracing"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "deeply supervised knowledge synergy"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge transfer with jacobian matching"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep supervision with intermediate concepts"
    - "deep co training for semi supervised image recognition"
    - "deeptravel a neural network based travel time estimation model with auxiliary supervision"
    - "modality distillation with multiple stream networks for action recognition"

Connected through hyperedge HE-5 (induced by source paper "gesture to gesture translation in the wild via category independent conditional maps"):
  with members:
    - "3d guided fine grained face manipulation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "pros and cons of gan evaluation measures"
    - "triple consistency loss for pairing distributions in gan based face synthesis"
    - "instagan instance aware image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "animating arbitrary objects via deep motion transfer"

Connected through hyperedge HE-6 (induced by source paper "recurrent u net for resource constrained segmentation"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "group normalization"
    - "analysis of hand segmentation in the wild"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "residual conv deconv grid network for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "a topological loss function for deep learning based image segmentation using persistent homology"):
  with members:
    - "phom gem persistent homology for generative models"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "a topology layer for machine learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "connectivity optimized representation learning via persistent homology"
    - "toporeg a topological regularizer for classifiers"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"

Connected through hyperedge HE-8 (induced by source paper "topology preserving deep image segmentation"):
  with members:
    - "a topological regularizer for classifiers via persistent homology"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "sliced wasserstein kernel for persistence diagrams"
    - "deep learning with topological signatures"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image 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)