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: "learning to learn stochastic gradient descent with biased regularization"
  Abstract: We study the problem of learning-to-learn: inferring a learning algorithm that works well on tasks sampled from an unknown distribution. As class of algorithms we consider Stochastic Gradient Descent on the true risk regularized by the square euclidean distance to a bias vector. We present an average excess risk bound for such a learning algorithm. This result quantifies the potential benefit of using a bias vector with respect to the unbiased case. We then address the problem of estimating the bias from a sequence of tasks. We propose a meta-algorithm which incrementally updates the bias, as new tasks are observed. The low space and time complexity of this approach makes it appealing in practice. We provide guarantees on the learning ability of the meta-algorithm. A key feature of our results is that, when the number of tasks grows and their variance is relatively small, our learning-to-learn approach has a significant advantage over learning each task in isolation by Stochastic Gradient Descent without a bias term. We report on numerical experiments which demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "multi step model agnostic meta learning convergence and improved algorithms"):
  with members:
    - "provably convergent policy gradient methods for model agnostic meta reinforcement learning"
    - "distribution agnostic model agnostic meta learning"
    - "es maml simple hessian free meta learning"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "online meta learning"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "online meta learning"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "provable guarantees for gradient based meta learning"
    - "characterizing and avoiding negative transfer"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-3 (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"
    - "meta learning with differentiable convex optimization"
    - "online meta learning"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with differentiable closed form solvers"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-4 (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"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "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-5 (induced by source paper "online meta learning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "introduction to online convex optimization"
    - "provable guarantees for gradient based meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "truncated back propagation for bilevel optimization"
    - "how to train your maml"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "deep online learning via meta learning continual adaptation for model based rl"

Connected through hyperedge HE-6 (induced by source paper "modular meta learning with shrinkage"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "optimizing millions of hyperparameters by implicit differentiation"
    - "meta learning with differentiable convex optimization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-7 (induced by source paper "provable representation learning for imitation learning via bi level optimization"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "online meta learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "batch policy learning under constraints"
    - "meta learning with implicit gradients"
    - "imitating latent policies from observation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-8 (induced by source paper "adaptive gradient based meta learning methods"):
  with members:
    - "online meta learning"
    - "introduction to online convex optimization"
    - "leaf a benchmark for federated settings"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "federated meta learning for recommendation"
    - "prototypical networks for few shot learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "online linear optimization with the log determinant regularizer"

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)