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: "learning gradient descent better generalization and longer horizons"
  Abstract: Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters. Trying different combinations can be quite labor-intensive and time consuming. Recently, researchers have tried to use deep learning algorithms to exploit the landscape of the loss function of the training problem of interest, and learn how to optimize over it in an automatic way. In this paper, we propose a new learning-to-learn model and some useful and practical tricks. Our optimizer outperforms generic, hand-crafted optimization algorithms and state-of-the-art learning-to-learn optimizers by DeepMind in many tasks. We demonstrate the effectiveness of our algorithms on a number of tasks, including deep MLPs, CNNs, and simple LSTMs.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "roam recurrently optimizing tracking model"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "learning to learn by zeroth order oracle"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "learning to optimize neural nets"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "query efficient hard label black box attack an optimization based approach"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "understanding and correcting pathologies in the training of learned optimizers"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "understanding short horizon bias in stochastic meta optimization"
    - "learning to optimize neural nets"
    - "aggregated momentum stability through passive damping"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "unbiasing truncated backpropagation through time"
    - "evolved policy gradients"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-7 (induced by source paper "meta continual learning"):
  with members:
    - "online learning of a memory for learning rates"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "encoder based lifelong learning"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting by incremental moment matching"

Connected through hyperedge HE-8 (induced by source paper "hyperadam a learnable task adaptive adam for network training"):
  with members:
    - "on the convergence of adam and beyond"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "learned optimizers that scale and generalize"
    - "listening to the world improves speech command recognition"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"

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)