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: "rapid learning or feature reuse towards understanding the effectiveness of maml"
  Abstract: An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks. Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse, with the meta initialization already containing high quality features? We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor. This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of a MAML-trained network. ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML. We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm). We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.

Connected through hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning"):
  with members:
    - "learning to continually learn"
    - "fine grained continual learning"
    - "continuous meta learning without tasks"
    - "reinforcement learning in non stationary environments"
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "regularization shortcomings for continual learning"

Connected through hyperedge HE-2 (induced by source paper "domain adaptive few shot learning"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "meta learning with differentiable convex optimization"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "a closer look at few shot classification"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "moment matching for multi source domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "meta learning initializations for low resource drug discovery"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "pre training graph neural networks"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "potentialnet for molecular property prediction"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-4 (induced by source paper "meta learning framework with applications to zero shot time series forecasting"):
  with members:
    - "energy predictive models with limited data using transfer learning"
    - "decentralized flood forecasting using deep neural networks"
    - "financial time series forecasting with deep learning a systematic literature review 2005 2019"
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "meta learning with latent embedding optimization"
    - "transfer learning for time series classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "few shot learning via learning the representation provably"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning with differentiable convex optimization"
    - "online meta learning"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "meta learning with differentiable closed form solvers"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-6 (induced by source paper "a sample complexity separation between non convex and convex meta learning"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "decoupling adaptation from modeling with meta optimizers for meta learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "a mathematical theory of semantic development in deep neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning"):
  with members:
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deep speaker embedding learning with multi level pooling for text independent speaker verification"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "decoupling adaptation from modeling with meta optimizers for meta learning"):
  with members:
    - "meta curvature"
    - "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"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "towards understanding generalization in gradient based meta 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)