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: "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
  Abstract: This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1 mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.

Connected through hyperedge HE-1 (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"
    - "no new net"
    - "autofocus layer for semantic segmentation"
    - "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-2 (induced by source paper "brain tumor segmentation on mri with missing modalities"):
  with members:
    - "deep recurrent level set for segmenting brain tumors"
    - "deep depth completion of a single rgb d image"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "hemis hetero modal image segmentation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "improving 3d u net for brain tumor segmentation by utilizing lesion prior"):
  with members:
    - "on the convergence of adam and beyond"
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "group normalization"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "accurate 3d cell segmentation using deep features and crf refinement"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "group normalization"
    - "cell segmentation in 3d confocal images using supervoxel merge forests with cnn based hypothesis selection"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-5 (induced by source paper "prediction of overall survival of brain tumor patients"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"

Connected through hyperedge HE-6 (induced by source paper "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"):
  with members:
    - "a pretrained densenet encoder for brain tumor segmentation"
    - "deep learning versus classical regression for brain tumor patient survival prediction"
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "hierarchical multi class segmentation of glioma images using networks with multi level activation function"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "glioma segmentation with cascaded unet"
    - "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"
    - "brain tumor segmentation using an ensemble of 3d u nets and overall survival prediction using radiomic features"

Connected through hyperedge HE-7 (induced by source paper "normalization in training u net for 2d biomedical semantic segmentation"):
  with members:
    - "group normalization"
    - "fully automatic detection and segmentation of abdominal aortic thrombus in post operative cta images using deep convolutional neural networks"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "towards automatic 3d shape instantiation for deployed stent grafts 2d multiple class and class imbalance marker segmentation with equally weighted focal u net"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "don t decay the learning rate increase the batch size"
    - "a survey on deep learning in medical image analysis"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"

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)