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: "adaptive gradient based meta learning methods"
  Abstract: We build a theoretical framework for understanding practical meta-learning methods that enables the integration of sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms. Our approach enables the task-similarity to be learned adaptively, provides sharper transfer-risk bounds in the setting of statistical learning-to-learn, and leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure. We use our theory to modify several popular meta-learning algorithms and improve their training and meta-test-time performance on standard problems in few-shot and federated deep learning.

Connected through hyperedge HE-1 (induced by source paper "survey of personalization techniques for federated learning"):
  with members:
    - "three approaches for personalization with applications to federated learning"
    - "personalized federated learning for intelligent iot applications a cloud edge based framework"
    - "data selection for federated learning with relevant and irrelevant data at clients"
    - "federated learning of a mixture of global and local models"
    - "salvaging federated learning by local adaptation"
    - "personalized federated learning a meta learning approach"
    - "federated learning with personalization layers"
    - "advances and open problems in federated learning"

Connected through hyperedge HE-2 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "advances and open problems in federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "variational federated multi task learning"
    - "variance reduced local sgd with lower communication complexity"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "semi cyclic stochastic gradient descent"

Connected through hyperedge HE-3 (induced by source paper "personalized federated learning a meta learning approach"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "on the convergence of local descent methods in federated learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "hyper sphere quantization communication efficient sgd for federated learning"

Connected through hyperedge HE-4 (induced by source paper "dynamic federated learning"):
  with members:
    - "variational federated multi task learning"
    - "asynchronous federated optimization"
    - "agnostic federated learning"
    - "client edge cloud hierarchical federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-5 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "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-6 (induced by source paper "provably convergent policy gradient methods for model agnostic meta reinforcement learning"):
  with members:
    - "guided meta policy search"
    - "online meta learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "meta learning with implicit gradients"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-7 (induced by source paper "few shot learning via learning the representation provably"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "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"
    - "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-8 (induced by source paper "geometric dataset distances via optimal transport"):
  with members:
    - "dynamics and reachability of learning tasks"
    - "hierarchical optimal transport for document representation"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "learning embeddings into entropic wasserstein spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep learning for classical japanese literature"
    - "deep contextualized word representations"

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)