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: "evolutionary stochastic gradient descent for optimization of deep neural networks"
  Abstract: We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks. ESGD combines SGD and gradient-free evolutionary algorithms as complementary algorithms in one framework in which the optimization alternates between the SGD step and evolution step to improve the average fitness of the population. With a back-off strategy in the SGD step and an elitist strategy in the evolution step, it guarantees that the best fitness in the population will never degrade. In addition, individuals in the population optimized with various SGD-based optimizers using distinct hyper-parameters in the SGD step are considered as competing species in a coevolution setting such that the complementarity of the optimizers is also taken into account. The effectiveness of ESGD is demonstrated across multiple applications including speech recognition, image recognition and language modeling, using networks with a variety of deep architectures.

Connected through hyperedge HE-1 (induced by source paper "autoencoding with xcsf"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "weight agnostic neural networks"
    - "autoencoding with a learning classifier system initial results"
    - "population based training of neural networks"
    - "convolution by evolution differentiable pattern producing networks"
    - "a cognitive architecture based on a learning classifier system with spiking classifiers"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "acoustic model optimization based on evolutionary stochastic gradient descent with anchors for automatic speech recognition"):
  with members:
    - "es is more than just a traditional finite difference approximator"
    - "population based training of neural networks"
    - "the cma evolution strategy a tutorial"
    - "adam a method for stochastic optimization"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-3 (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"
    - "evolved policy gradients"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "online learning rate adaptation with hypergradient descent"

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)