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: "improving cnn training using disentanglement for liver lesion classification in ct"
  Abstract: Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network based solutions using synthetic data. A key factor in the generation of new samples is controlling the important appearance features and potentially being able to generate a new sample of a specific class with different variants. In this work we suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data. Our experiments on liver lesion classification in CT show an average improvement of 7.4% in accuracy over the baseline training scheme.

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"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"

Connected through hyperedge HE-2 (induced by source paper "representation disentanglement for multi task learning with application to fetal ultrasound"):
  with members:
    - "a two step disentanglement method"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "image to image translation for cross domain disentanglement"
    - "dltk state of the art reference implementations for deep learning on medical images"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

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)