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: "faster sgd using sketched conditioning"
  Abstract: We propose a novel method for speeding up stochastic optimization algorithms via sketching methods, which recently became a powerful tool for accelerating algorithms for numerical linear algebra. We revisit the method of conditioning for accelerating first-order methods and suggest the use of sketching methods for constructing a cheap conditioner that attains a significant speedup with respect to the Stochastic Gradient Descent (SGD) algorithm. While our theoretical guarantees assume convexity, we discuss the applicability of our method to deep neural networks, and experimentally demonstrate its merits.

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"
    - "the case for full matrix adaptive regularization"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "from persistent homology to reinforcement learning with applications for retail banking"):
  with members:
    - "non negative paratuck2 tensor decomposition combined to lstm network for smart contracts profiling"
    - "user device authentication in mobile banking using aphen for paratuck2 tensor decomposition"
    - "phom gem persistent homology for generative models"
    - "predicting sparse clients actions with cpopt net in the banking environment"
    - "shampoo preconditioned stochastic tensor optimization"
    - "neural tensor factorization"
    - "implicit quantile networks for distributional reinforcement learning"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "the case for full matrix adaptive regularization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "eigenvalue decay implies polynomial time learnability for neural networks"
    - "efficient second order online learning by sketching"
    - "solving ridge regression using sketched preconditioned svrg"
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "faster kernel ridge regression using sketching and preconditioning"

Connected through hyperedge HE-5 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "randomized sketches of convex programs with sharp guarantees"
    - "mini batch primal and dual methods for svms"
    - "recovering the optimal solution by dual random projection"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "shampoo preconditioned stochastic tensor optimization"):
  with members:
    - "introduction to online convex optimization"
    - "attention is all you need"
    - "a unified approach to adaptive regularization in online and stochastic optimization"
    - "understanding deep learning requires rethinking generalization"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "path sgd path normalized optimization in deep neural networks"
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-7 (induced by source paper "efficient second order online learning by sketching"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "efficient frequent directions algorithm for sparse matrices"
    - "the fast convergence of incremental pca"
    - "scale free algorithms for online linear optimization"
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "one pass auc 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.NA(Numerical Analysis)