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: "injecting external solutions into cma es"
  Abstract: This report considers how to inject external candidate solutions into the CMA-ES algorithm. The injected solutions might stem from a gradient or a Newton step, a surrogate model optimizer or any other oracle or search mechanism. They can also be the result of a repair mechanism, for example to render infeasible solutions feasible. Only small modifications to the CMA-ES are necessary to turn injection into a reliable and effective method: too long steps need to be tightly renormalized. The main objective of this report is to reveal this simple mechanism. Depending on the source of the injected solutions, interesting variants of CMA-ES arise. When the best-ever solution is always (re-)injected, an elitist variant of CMA-ES with weighted multi-recombination arises. When \emph{all} solutions are injected from an \emph{external} source, the resulting algorithm might be viewed as \emph{adaptive encoding} with step-size control. In first experiments, injected solutions of very good quality lead to a convergence speed twice as fast as on the (simple) sphere function without injection. This means that we observe an impressive speed-up on otherwise difficult to solve functions. Single bad injected solutions on the other hand do no significant harm.

Connected through hyperedge HE-1 (induced by source paper "guided evolutionary strategies escaping the curse of dimensionality in random search"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "understanding short horizon bias in stochastic meta optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "world models"
    - "evolutionary stochastic gradient descent for optimization of deep neural networks"
    - "evolved policy gradients"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"

Connected through hyperedge HE-2 (induced by source paper "guided evolutionary strategies augmenting random search with surrogate gradients"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "understanding short horizon bias in stochastic meta optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "evolved policy gradients"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "online learning rate adaptation with hypergradient descent"
    - "safe mutations for deep and recurrent neural networks through output gradients"

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)