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: "unit tests for stochastic optimization"
  Abstract: Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms. A wide variety of such optimization algorithms have been devised; however, it is unclear whether these algorithms are robust and widely applicable across many different optimization landscapes. In this paper we develop a collection of unit tests for stochastic optimization. Each unit test rapidly evaluates an optimization algorithm on a small-scale, isolated, and well-understood difficulty, rather than in real-world scenarios where many such issues are entangled. Passing these unit tests is not sufficient, but absolutely necessary for any algorithms with claims to generality or robustness. We give initial quantitative and qualitative results on numerous established algorithms. The testing framework is open-source, extensible, and easy to apply to new algorithms.

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 "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-3 (induced by source paper "deepobs a deep learning optimizer benchmark suite"):
  with members:
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "bda pch block diagonal approximation of positive curvature hessian for training neural networks"
    - "know what you don t know unanswerable questions for squad"
    - "block diagonal hessian free optimization for training neural networks"
    - "tbd benchmarking and analyzing deep neural network training"
    - "attention is all you need"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-4 (induced by source paper "rover descent learning to optimize by learning to navigate on prototypical loss surfaces"):
  with members:
    - "learning to optimize neural nets"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "the cma evolution strategy a tutorial"
    - "continuous control with deep reinforcement learning"
    - "memory based control with recurrent neural networks"
    - "optimizing neural networks with kronecker factored approximate curvature"

Connected through hyperedge HE-5 (induced by source paper "variants of rmsprop and adagrad with logarithmic regret bounds"):
  with members:
    - "introduction to online convex optimization"
    - "a unified approach to adaptive regularization in online and stochastic optimization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "an overview of gradient descent optimization algorithms"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "equilibrated adaptive learning rates for non convex optimization"

Connected through hyperedge HE-6 (induced by source paper "minimalist regression network with reinforced gradients and weighted estimates a case study on parameters estimation in automated welding"):
  with members:
    - "noisy activation functions"
    - "learning to track at 100 fps with deep regression networks"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-7 (induced by source paper "bidirectional helmholtz machines"):
  with members:
    - "importance weighted autoencoders"
    - "blocks and fuel frameworks for deep learning"
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "iterative refinement of approximate posterior for training directed belief networks"
    - "adam a method for stochastic optimization"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "accurate and conservative estimates of mrf log likelihood using reverse annealing"

Connected through hyperedge HE-8 (induced by source paper "equilibrated adaptive learning rates for non convex optimization"):
  with members:
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "revisiting natural gradient for deep networks"
    - "adadelta an adaptive learning rate method"
    - "estimating the hessian by back propagating curvature"
    - "theano new features and speed improvements"

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)