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 "transfusion understanding transfer learning for medical imaging"):
  Abstract: Transfer learning from natural image datasets, particularly ImageNet, using standard large models and corresponding pretrained weights has become a de-facto method for deep learning applications to medical imaging. However, there are fundamental differences in data sizes, features and task specifications between natural image classification and the target medical tasks, and there is little understanding of the effects of transfer. In this paper, we explore properties of transfer learning for medical imaging. A performance evaluation on two large scale medical imaging tasks shows that surprisingly, transfer offers little benefit to performance, and simple, lightweight models can perform comparably to ImageNet architectures. Investigating the learned representations and features, we find that some of the differences from transfer learning are due to the over-parametrization of standard models rather than sophisticated feature reuse. We isolate where useful feature reuse occurs, and outline the implications for more efficient model exploration. We also explore feature independent benefits of transfer arising from weight scalings.

Includes members:
  Member "similarity of neural network representations revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "investigating multilingual nmt representations at scale":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "contextual lensing of universal sentence representations")
  Member "the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fixed encoder self attention patterns in transformer based machine translation")
  Member "rethinking imagenet pre training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "direct uncertainty prediction for medical second opinions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to switch between machines and humans")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "a closer look at deep learning heuristics learning rate restarts warmup and distillation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mti net multi scale task interaction networks for multi task learning")

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)