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 "domain adaptive transfer learning with specialist models"):
  Abstract: Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. We find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. These findings are important given the continued increase in dataset sizes. We further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. Our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning. Our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice.

Includes members:
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "exploring the limits of weakly supervised pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "deep layer aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "speed accuracy trade offs for modern convolutional object detectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")

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)