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: "communication optimal parallel and sequential cholesky decomposition"
  Abstract: Numerical algorithms have two kinds of costs: arithmetic and communication, by which we mean either moving data between levels of a memory hierarchy (in the sequential case) or over a network connecting processors (in the parallel case). Communication costs often dominate arithmetic costs, so it is of interest to design algorithms minimizing communication. In this paper we first extend known lower bounds on the communication cost (both for bandwidth and for latency) of conventional ($O(n^3)$) matrix multiplication to Cholesky factorization, which is used for solving dense symmetric positive definite linear systems. Second, we compare the costs of various Cholesky decomposition implementations to these lower bounds and identify the algorithms and data structures that attain them. In the sequential case, we consider both the two-level and hierarchical memory models. Combined with prior results in [J. Demmel et al., Communication-optimal Parallel and Sequential QR and LU Factorizations, Technical report EECS-2008-89, University of California, Berkeley, CA, 2008], [J. Demmel et al., Implementing Communication-optimal Parallel and Sequential QR and LU Factorizations, SIAM. J. Sci. Comp., submitted], and [J. Demmel, L. Grigori, and H. Xiang, Communication-avoiding Gaussian Elimination, Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, 2008] this gives a set of communication-optimal algorithms for $O(n^3)$ implementations of the three basic factorizations of dense linear algebra: LU with pivoting, QR, and Cholesky. But it goes beyond this prior work on sequential LU by optimizing communication for any number of levels of memory hierarchy.

Connected through hyperedge HE-1 (induced by source paper "serverless straggler mitigation using local error correcting codes"):
  with members:
    - "polar coded distributed matrix multiplication"
    - "oversketched newton fast convex optimization for serverless systems"
    - "cloud programming simplified a berkeley view on serverless computing"
    - "collaborative decoding of polynomial codes for distributed computation"
    - "serverless computing one step forward two steps back"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"

Connected through hyperedge HE-2 (induced by source paper "the i o complexity of hybrid algorithms for square matrix multiplication"):
  with members:
    - "the i o complexity of strassen s matrix multiplication with recomputation"
    - "powers of tensors and fast matrix multiplication"
    - "communication lower bounds for distributed memory computations"
    - "the input output complexity of sparse matrix multiplication"
    - "communication lower bounds for distributed memory computations"
    - "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-3 (induced by source paper "improved parallel cache oblivious algorithms for dynamic programming and linear algebra"):
  with members:
    - "persistent memory i o primitives"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "algorithmic building blocks for asymmetric memories"
    - "the parallel persistent memory model"
    - "resource oblivious sorting on multicores"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"

Connected through hyperedge HE-4 (induced by source paper "numpywren serverless linear algebra"):
  with members:
    - "ray a distributed framework for emerging ai applications"
    - "occupy the cloud distributed computing for the 99"
    - "mllib machine learning in apache spark"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-5 (induced by source paper "the i o complexity of strassen s matrix multiplication with recomputation"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "communication lower bounds for distributed memory computations"
    - "the input output complexity of sparse matrix multiplication"
    - "communication lower bounds for distributed memory computations"
    - "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
    - "faster algorithms for rectangular matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-6 (induced by source paper "minimizing communication in linear algebra"):
  with members:
    - "lu factorization with panel rank revealing pivoting and its communication avoiding version"
    - "graph expansion and communication costs of fast matrix multiplication regular submission"
    - "hybrid static dynamic scheduling for already optimized dense matrix factorization"
    - "a class of parallel tiled linear algebra algorithms for multicore architectures"

Connected through hyperedge HE-7 (induced by source paper "graph expansion and communication costs of fast matrix multiplication regular submission"):
  with members:
    - "minimizing communication in linear algebra"

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)