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 adaptive s step conjugate gradient method"
  Abstract: On modern large-scale parallel computers, the performance of Krylov subspace iterative methods is limited by global synchronization. This has inspired the development of $s$-step Krylov subspace method variants, in which iterations are computed in blocks of $s$, which can reduce the number of global synchronizations per iteration by a factor of $O(s)$. #R##N#Although the $s$-step variants are mathematically equivalent to their classical counterparts, they can behave quite differently in finite precision depending on the parameter $s$. If $s$ is chosen too large, the $s$-step method can suffer a convergence delay and a decrease in attainable accuracy relative to the classical method. This makes it difficult for a potential user of such methods - the $s$ value that minimizes the time per iteration may not be the best $s$ for minimizing the overall time-to-solution, and further may cause an unacceptable decrease in accuracy. #R##N#Towards improving the reliability and usability of $s$-step Krylov subspace methods, in this work we derive the \emph{adaptive $s$-step CG method}, a variable $s$-step CG method where in block $k$, the parameter $s_k$ is determined automatically such that a user-specified accuracy is attainable. The method for determining $s_k$ is based on a bound on growth of the residual gap within block $k$, from which we derive a constraint on the condition numbers of the computed $O(s_k)$-dimensional Krylov subspace bases. The computations required for determining the block size $s_k$ can be performed without increasing the number of global synchronizations per block. Our numerical experiments demonstrate that the adaptive $s$-step CG method is able to attain up to the same accuracy as classical CG while still significantly reducing the total number of global synchronizations.

Connected through hyperedge HE-1 (induced by source paper "scalable resilience against node failures for communication hiding preconditioned conjugate gradient and conjugate residual methods"):
  with members:
    - "improving strong scaling of the conjugate gradient method for solving large linear systems using global reduction pipelining"
    - "how to make the preconditioned conjugate gradient method resilient against multiple node failures"
    - "numerically stable recurrence relations for the communication hiding pipelined conjugate gradient method"
    - "the communication hiding conjugate gradient method with deep pipelines"
    - "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"
    - "numerically stable variants of the communication hiding pipelined conjugate gradients algorithm for the parallel solution of large scale symmetric linear systems"
    - "a backward forward recovery approach for the preconditioned conjugate gradient method"

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)