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: "autofocus layer for semantic segmentation"
  Abstract: We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale-invariant, with only a modest increase in the number of parameters. The proposed autofocus layer can be easily integrated into existing networks to improve a model's representational power. We evaluate our models on the challenging tasks of multi-organ segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very promising performance.

Connected through hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction"):
  with members:
    - "boundary weighted domain adaptive neural network for prostate mr image segmentation"
    - "learning a deep convnet for multi label classification with partial labels"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "feature fusion encoder decoder network for automatic liver lesion segmentation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "the liver tumor segmentation benchmark lits"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "acfnet attentional class feature network for semantic segmentation"):
  with members:
    - "shapemask learning to segment novel objects by refining shape priors"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "adaptive weighting multi field of view cnn for semantic segmentation in pathology"):
  with members:
    - "improving whole slide segmentation through visual context a systematic study"
    - "switching convolutional neural network for crowd counting"
    - "squeeze and excitation networks"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "learning convolutional networks for content weighted image compression"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"

Connected through hyperedge HE-5 (induced by source paper "3d dilated multi fiber network for real time brain tumor segmentation in mri"):
  with members:
    - "multi fiber networks for video recognition"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "brain tumor segmentation with deep neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-6 (induced by source paper "nnu net breaking the spell on successful medical image segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "the liver tumor segmentation benchmark lits"
    - "why rankings of biomedical image analysis competitions should be interpreted with care"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "attention u net learning where to look for the pancreas"
    - "cnn based segmentation of medical imaging data"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "patchfcn for intracranial hemorrhage detection"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "dilated residual networks"
    - "application of deep learning in neuroradiology automated detection of basal ganglia hemorrhage using 2d convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

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)