human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "disentangle align and fuse for multimodal and zero shot image segmentation"):
  Abstract: Magnetic resonance (MR) protocols rely on several sequences to properly assess pathology and organ status. Yet, despite advances in image analysis we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the information shared between modalities (largely an organ's anatomy) is beneficial for multi-modality multi-input processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to claim this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, enabling semi-supervised and zero shot learning. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, that non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data, and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation.

Includes members:
  Member "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion":
    also connected through other hyperedges:
      (none)
  Member "theory and evaluation metrics for learning disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "instance invariant adaptive object detection via progressive disentanglement")
  Member "transgaga geometry aware unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning cross domain generalizable features by representation disentanglement")
  Member "unsupervised deformable registration for multi modal images via disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "representation disentanglement for multi task learning with application to fetal ultrasound":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning cross domain generalizable features by representation disentanglement")
  Member "factorised spatial representation learning application in semi supervised myocardial segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion")

Question: Please tell me which class the source paper 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)