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: "task agnostic meta learning for few shot learning"
  Abstract: Meta-learning approaches have been proposed to tackle the few-shot learning problem.Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined.Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.

Connected through hyperedge HE-1 (induced by source paper "learning to compare relation semantic alignment for few shot learning"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "dense classification and implanting for few shot learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"

Connected through hyperedge HE-2 (induced by source paper "meta learning across meta tasks for few shot learning"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "learning what and where to transfer"
    - "charting the right manifold manifold mixup for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "transductive episodic wise adaptive metric for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "local nonparametric meta learning"):
  with members:
    - "adaptive deep kernel learning"
    - "meta learning without memorization"
    - "towards understanding generalization in gradient based meta learning"
    - "attentive neural processes"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "fast context adaptation via meta learning"
    - "bayesian model agnostic meta learning"
    - "conditional neural processes"

Connected through hyperedge HE-4 (induced by source paper "fast adaptation to super resolution networks via meta learning"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "on first order meta learning algorithms"
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "meta reinforcement learning of structured exploration strategies"
    - "meta transfer learning for few shot learning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "asymmetric distribution measure for few shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "meta learning for semi supervised few shot classification"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "meta transfer learning for few shot learning"
    - "began boundary equilibrium generative adversarial networks"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-7 (induced by source paper "metaadvdet towards robust detection of evolving adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "spatially transformed adversarial examples"
    - "detecting adversarial examples via neural fingerprinting"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "rethinking feature distribution for loss functions in image classification"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-8 (induced by source paper "few shot learning with contextual cueing for object recognition in complex scenes"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "dense classification and implanting for few shot learning"
    - "context aware zero shot learning for object recognition"
    - "few shot learning via saliency guided hallucination of samples"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "tafe net task aware feature embeddings for low shot learning"
    - "image deformation meta networks for one shot learning"

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.LG(Machine Learning)