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: "meta curvature"
  Abstract: We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation. MC expands on the model-agnostic meta-learner (MAML) by learning to transform the gradients in the inner optimization such that the transformed gradients achieve better generalization performance to a new task. For training large scale neural networks, we decompose the curvature matrix into smaller matrices in a novel scheme where we capture the dependencies of the model's parameters with a series of tensor products. We demonstrate the effects of our proposed method on several few-shot learning tasks and datasets. Without any task specific techniques and architectures, the proposed method achieves substantial improvement upon previous MAML variants and outperforms the recent state-of-the-art methods. Furthermore, we observe faster convergence rates of the meta-training process. Finally, we present an analysis that explains better generalization performance with the meta-trained curvature.

Connected through hyperedge HE-1 (induced by source paper "incremental object detection via meta learning"):
  with members:
    - "rilod near real time incremental learning for object detection at the edge"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "meta learning without memorization"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "efficient lifelong learning with a gem"
    - "few shot object detection via feature reweighting"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-2 (induced by source paper "decoupling adaptation from modeling with meta optimizers for meta learning"):
  with members:
    - "meta descent for online continual prediction"
    - "meta learning with warped gradient descent"
    - "alpha maml adaptive model agnostic meta learning"
    - "using learned optimizers to make models robust to input noise"
    - "meta learning via learned loss"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "towards understanding generalization in gradient based meta learning"

Connected through hyperedge HE-3 (induced by source paper "meta neighborhoods"):
  with members:
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "self supervised generalisation with meta auxiliary learning"
    - "a closer look at few shot classification"
    - "robust classification with convolutional prototype learning"
    - "meta learning with differentiable closed form solvers"
    - "memory matching networks for one shot image recognition"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"

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)