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 "multimodal self supervised learning for medical image analysis"):
  Abstract: In this paper, we propose a self-supervised learning approach that leverages multiple imaging modalities to increase data efficiency for medical image analysis. To this end, we introduce multimodal puzzle-solving proxy tasks, which facilitate neural network representation learning from multiple image modalities. These representations allow for subsequent fine-tuning on different downstream tasks. To achieve that, we employ the Sinkhorn operator to predict permutations of puzzle pieces in conjunction with a modality agnostic feature embedding. Together, they allow for a lean network architecture and increased computational efficiency. Under this framework, we propose different strategies for puzzle construction, integrating multiple medical imaging modalities, with varying levels of puzzle complexity. We benchmark these strategies in a range of experiments to assess the gains of our method in downstream performance and data-efficiency on different target tasks. Our experiments show that solving puzzles interleaved with multimodal content yields more powerful semantic representations. This allows us to solve downstream tasks more accurately and efficiently, compared to treating each modality independently. We demonstrate the effectiveness of the proposed approach on two multimodal medical imaging benchmarks: the BraTS and the Prostate semantic segmentation datasets, on which we achieve competitive results to state-of-the-art solutions, at a fraction of the computational expense. We also outperform many previous solutions on the chosen benchmarks.

Includes members:
  Member "a large annotated medical image dataset for the development and evaluation of segmentation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the future of digital health with federated learning")
  Member "domain generalization by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain adaptive ensemble learning")
  Member "deep learning versus classical regression for brain tumor patient survival prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge")
  Member "non rigid image registration using self supervised fully convolutional networks without training data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comparative study for non rigid image registration and rigid image registration")
  Member "no new net":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation")
  Member "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "precomputed real time texture synthesis with markovian generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "unsupervised learning of visual representations by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)