human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "boosting supervision with self supervision for few shot learning"
  Abstract: We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. While recent approaches for self-supervised learning have shown the benefits of training on large unlabeled datasets, we find improvements in generalization even on small datasets and when combined with strong supervision. Learning representations with self-supervised losses reduces the relative error rate of a state-of-the-art meta-learner by 5-25% on several few-shot learning benchmarks, as well as off-the-shelf deep networks on standard classification tasks when training from scratch. We find the benefits of self-supervision increase with the difficulty of the task. Our approach utilizes the images within the dataset to construct self-supervised losses and hence is an effective way of learning transferable representations without relying on any external training data.

Connected through hyperedge HE-1 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "moments in time dataset one million videos for event understanding"
    - "how to train your maml"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation through self supervision"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "domain generalization by solving jigsaw puzzles"
    - "stabilizing gan training with multiple random projections"
    - "deep visual domain adaptation a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "representation learning with contrastive predictive coding"

Question: Please tell me which class the center node 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)