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: "hemis hetero modal image segmentation"
  Abstract: We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.

Connected through hyperedge HE-1 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"):
  with members:
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "brain tumor segmentation on mri with missing modalities"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "multimodal unsupervised image to image translation"
    - "learning factorized multimodal representations"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "improving cnn training using disentanglement for liver lesion classification in ct"
    - "scalable multimodal convolutional networks for brain tumour segmentation"

Connected through hyperedge HE-2 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"

Connected through hyperedge HE-3 (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"
    - "brain tumor segmentation and tractographic feature extraction from structural mr images for overall survival prediction"
    - "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"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "joint learning of brain lesion and anatomy segmentation from heterogeneous datasets"):
  with members:
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "sub cortical brain structure segmentation using f cnn s"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "a unified representation network for segmentation with missing modalities"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "a survey on deep learning in medical image analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "moddrop adaptive multi modal gesture recognition"

Connected through hyperedge HE-6 (induced by source paper "multi modal segmentation with missing mr sequences using pre trained fusion networks"):
  with members:
    - "no new net"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "mmfnet a multi modality mri fusion network for segmentation of nasopharyngeal carcinoma"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "yolov3 an incremental improvement"
    - "3d roi aware u net for accurate and efficient colorectal tumor segmentation"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "cbam convolutional block attention module"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"

Connected through hyperedge HE-8 (induced by source paper "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks"):
  with members:
    - "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "spinal cord gray matter segmentation using deep dilated convolutions"
    - "domain adaptation for mri organ segmentation using reverse classification accuracy"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "semi supervised deep learning for fully convolutional 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)