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: "gadam genetic evolutionary adam for deep neural network optimization"
  Abstract: Deep neural network learning can be formulated as a non-convex optimization problem. Existing optimization algorithms, e.g., Adam, can learn the models fast, but may get stuck in local optima easily. In this paper, we introduce a novel optimization algorithm, namely GADAM (Genetic-Evolutionary Adam). GADAM learns deep neural network models based on a number of unit models generations by generations: it trains the unit models with Adam, and evolves them to the new generations with genetic algorithm. We will show that GADAM can effectively jump out of the local optima in the learning process to obtain better solutions, and prove that GADAM can also achieve a very fast convergence. Extensive experiments have been done on various benchmark datasets, and the learning results will demonstrate the effectiveness and efficiency of the GADAM algorithm.

Connected through hyperedge HE-1 (induced by source paper "sadam a variant of adam for strongly convex functions"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "stronger baselines for trustable results in neural machine translation"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-2 (induced by source paper "gradient descent based optimization algorithms for deep learning models training"):
  with members:
    - "segen sample ensemble genetic evolutionary network model"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "adam a method for stochastic optimization"
    - "genetic algorithms for evolving deep neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "derivative free global optimization algorithms bayesian method and lipschitzian approaches"):
  with members:
    - "gradient descent based optimization algorithms for deep learning models training"
    - "derivative free global optimization algorithms population based methods and random search approaches"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "bgadam boosting based genetic evolutionary adam for convolutional neural network optimization"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "derivative free global optimization algorithms population based methods and random search approaches"):
  with members:
    - "derivative free global optimization algorithms bayesian method and lipschitzian approaches"
    - "gradient descent based optimization algorithms for deep learning models training"

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)