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: "the case for full matrix adaptive regularization"
  Abstract: Adaptive regularization methods come in diagonal and full-matrix variants. However, only the former have enjoyed widespread adoption in training large-scale deep models. This is due to the computational overhead of manipulating a full matrix in high dimension. In this paper, we show how to make full-matrix adaptive regularization practical and useful. We present GGT, a truly scalable full-matrix adaptive optimizer. At the heart of our algorithm is an efficient method for computing the inverse square root of a low-rank matrix. We show that GGT converges to first-order local minima, providing the first rigorous theoretical analysis of adaptive regularization in non-convex optimization. In preliminary experiments, GGT trains faster across a variety of synthetic tasks and standard deep learning benchmarks.

Connected through hyperedge HE-1 (induced by source paper "second order optimization made practical"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "introduction to online convex optimization"
    - "shampoo preconditioned stochastic tensor optimization"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "faster sgd using sketched conditioning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "matrix free preconditioning in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "attention is all you need"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"
    - "coin betting and parameter free online learning"

Connected through hyperedge HE-3 (induced by source paper "escaping saddle points with adaptive gradient methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "escaping saddle points in constrained optimization"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "a generic approach for escaping saddle points"

Connected through hyperedge HE-4 (induced by source paper "extreme tensoring for low memory preconditioning"):
  with members:
    - "introduction to online convex optimization"
    - "shampoo preconditioned stochastic tensor optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"
    - "a unified approach to adaptive regularization in online and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "memory efficient adaptive optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "shampoo preconditioned stochastic tensor optimization"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "stochastic gradient methods with block diagonal matrix adaptation"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "isolating sources of disentanglement in variational autoencoders"
    - "weighted adagrad with unified momentum"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "densely connected convolutional networks"
    - "adam a method for stochastic optimization"

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)