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 "probing the state of the art a critical look at visual representation evaluation"):
  Abstract: Self-supervised research improved greatly over the past half decade, with much of the growth being driven by objectives that are hard to quantitatively compare. These techniques include colorization, cyclical consistency, and noise-contrastive estimation from image patches. Consequently, the field has settled on a handful of measurements that depend on linear probes to adjudicate which approaches are the best. Our first contribution is to show that this test is insufficient and that models which perform poorly (strongly) on linear classification can perform strongly (weakly) on more involved tasks like temporal activity localization. Our second contribution is to analyze the capabilities of five different representations. And our third contribution is a much needed new dataset for temporal activity localization.

Includes members:
  Member "scaling and benchmarking self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "similarity of neural network representations revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "self supervised learning for video correspondence flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "learning correspondence from the cycle consistency of time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "the visual task adaptation benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "data efficient image recognition with contrastive predictive coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "revisiting self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "rethinking the faster r cnn architecture for temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised temporal action localization using deep metric 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.LG(Machine Learning)