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: "unsupervised deformable registration for multi modal images via disentangled representations"
  Abstract: We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learningbased image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation"):
  with members:
    - "fire unsupervised bi directional inter modality registration using deep networks"
    - "image and spatial transformer networks for structure guided image registration"
    - "unsupervised deformable image registration using cycle consistent cnn"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "multimodal unsupervised image to image translation"
    - "deep learning based inter modality image registration supervised by intra modality similarity"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "regnet multimodal sensor registration using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"):
  with members:
    - "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"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"

Connected through hyperedge HE-3 (induced by source paper "disentangle align and fuse for multimodal and zero shot image segmentation"):
  with members:
    - "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"
    - "theory and evaluation metrics for learning disentangled representations"
    - "transgaga geometry aware unsupervised image to image translation"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "temporal consistency objectives regularize the learning of disentangled representations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "recurrent neural networks for aortic image sequence segmentation with sparse annotations"
    - "learning to decompose and disentangle representations for video prediction"
    - "on the effectiveness of least squares generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-5 (induced by source paper "fire unsupervised bi directional inter modality registration using deep networks"):
  with members:
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "inverse consistent deep networks for unsupervised deformable image registration"
    - "spatial transformer 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)