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 "when does self supervision improve few shot learning"):
  Abstract: We present a technique to improve the generalization of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. Although recent research has shown benefits of self-supervised learning (SSL) on large unlabeled datasets, its utility on small datasets is unknown. We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets. The improvements are greater when the training set is smaller or the task is more challenging. Though the benefits of SSL may increase with larger training sets, we observe that SSL can have a negative impact on performance when there is a domain shift between distribution of images used for meta-learning and SSL. Based on this analysis we present a technique that automatically select images for SSL from a large, generic pool of unlabeled images for a given dataset using a domain classifier that provides further improvements. We present results using several meta-learners and self-supervised tasks across datasets with varying degrees of domain shifts and label sizes to characterize the effectiveness of SSL for few-shot learning.

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 "surprising effectiveness of few image unsupervised feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self labelling via simultaneous clustering and representation learning")
  Member "meta learning with differentiable convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "few shot learning with localization in realistic settings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "boosting few shot visual learning with self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "large scale adversarial representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "unsupervised pre training of image features on non curated data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "domain generalization by solving jigsaw puzzles":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain adaptive ensemble 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)