human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a bayesian variational framework for stochastic optimization"):
  Abstract: This work proposes a theoretical framework for stochastic optimization algorithms, based on a continuous Bayesian variational model for algorithms. Using techniques from stochastic control with asymmetric information, the solution to this variational problem is shown to be equivalent to a system of Forward Backward Differential Equations (FBSDEs). Using an analytical approximation to the solution of these FBSDEs, we recover a variety of existing adaptive stochastic gradient descent methods. This framework establishes a direct connection between stochastic optimization algorithms and a secondary Bayesian inference problem on gradients, where the prior and assumed observation dynamics determine the resulting algorithm.

Includes members:
  Member "aggregated momentum stability through passive damping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate")
  Member "a general system of differential equations to model first order adaptive algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "laprop a better way to combine momentum with adaptive gradient")
  Member "a unified approach to adaptive regularization in online and stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extreme tensoring for low memory preconditioning")
  Member "an overview of gradient descent optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)