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: "a survey of optimization methods from a machine learning perspective"
  Abstract: Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.

Connected through hyperedge HE-1 (induced by source paper "compositional adam an adaptive compositional solver"):
  with members:
    - "online meta learning"
    - "on the convergence of adam and beyond"
    - "derivative free order robust optimisation"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "learning to communicate implicitly by actions"
    - "accelerated method for stochastic composition optimization with nonsmooth regularization"

Connected through hyperedge HE-2 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate"):
  with members:
    - "calibrating the adaptive learning rate to improve convergence of adam"
    - "on empirical comparisons of optimizers for deep learning"
    - "decaying momentum helps neural network training"
    - "understanding the role of momentum in stochastic gradient methods"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "analysis of momentum methods"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-3 (induced by source paper "stable neural flows"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "dissecting neural odes"
    - "scalable gradients for stochastic differential equations"
    - "neural jump stochastic differential equations"
    - "graph neural ordinary differential equations"
    - "stable architectures for deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "transport analysis of infinitely deep neural network"

Connected through hyperedge HE-4 (induced by source paper "do optimization methods in deep learning applications matter"):
  with members:
    - "quasi newton optimization methods for deep learning applications"
    - "deep reinforcement learning via l bfgs optimization"
    - "imagenet training in minutes"
    - "is asgd accelerating asynchronous sgd using importance sampling"
    - "deep learning with elastic averaging sgd"

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)