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 "learning generalizable representations via diverse supervision"):
  Abstract: The problem of rare category recognition has received a lot of attention recently, with state-of-the-art methods achieving significant improvements. However, we identify two major limitations in the existing literature. First, the benchmarks are constructed by randomly splitting the categories of artificially balanced datasets into frequent (head), and rare (tail) subsets, which results in unrealistic category distributions in both of them. Second, the idea of using external sources of supervision to learn generalizable representations is largely overlooked. In this work, we attempt to address both of these shortcomings by introducing the ADE-FewShot benchmark. It stands upon the ADE dataset for scene parsing that features a realistic, long-tail distribution of categories as well as a diverse set of annotations. We turn it into a realistic few-shot classification benchmark by splitting the object categories into head and tail based on their distribution in the world. We then analyze the effect of applying various supervision sources on representation learning for rare category recognition, and observe significant improvements.

Includes members:
  Member "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "branched multi task networks deciding what layers to share")
  Member "boosting few shot visual learning with self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "lvis a dataset for large vocabulary instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equalization loss for long tailed object recognition")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "many task learning with task routing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task learning by a top down control network")
  Member "meta learning for semi supervised few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "multi task learning as multi objective optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "unsupervised representation learning by predicting image rotations":
    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)