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 "transfer learning in visual and relational reasoning"):
  Abstract: Transfer learning is becoming the de facto solution for vision and text encoders in the front-end processing of machine learning solutions. Utilizing vast amounts of knowledge in pre-trained models and subsequent fine-tuning allows achieving better performance in domains where labeled data is limited. In this paper, we analyze the efficiency of transfer learning in visual reasoning by introducing a new model (SAMNet) and testing it on two datasets: COG and CLEVR. Our new model achieves state-of-the-art accuracy on COG and shows significantly better generalization capabilities compared to the baseline. We also formalize a taxonomy of transfer learning for visual reasoning around three axes: feature, temporal, and reasoning transfer. Based on extensive experimentation of transfer learning on each of the two datasets, we show the performance of the new model along each axis.

Includes members:
  Member "leveraging medical visual question answering with supporting facts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pytorchpipe a framework for rapid prototyping of pipelines combining language and vision")
  Member "learning by abstraction the neural state machine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unbiased scene graph generation from biased training")
  Member "trends in integration of vision and language research a survey of tasks datasets and methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning cross modal context graph for visual grounding")
  Member "huggingface s transformers state of the art natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised question decomposition for question answering")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "transparency by design closing the gap between performance and interpretability in visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "object based reasoning in vqa":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "set structured latent representations")

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)