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: "parallel computation of echelon forms"
  Abstract: We propose efficient parallel algorithms and implementations on shared memory architectures of LU factorization over a finite field. Compared to the corresponding numerical routines, we have identified three main difficulties specific to linear algebra over finite fields. First, the arithmetic complexity could be dominated by modular reductions. Therefore, it is mandatory to delay as much as possible these reductions while mixing fine-grain parallelizations of tiled iterative and recursive algorithms. Second, fast linear algebra variants, e.g., using Strassen-Winograd algorithm, never suffer from instability and can thus be widely used in cascade with the classical algorithms. There, trade-offs are to be made between size of blocks well suited to those fast variants or to load and communication balancing. Third, many applications over finite fields require the rank profile of the matrix (quite often rank deficient) rather than the solution to a linear system. It is thus important to design parallel algorithms that preserve and compute this rank profile. Moreover, as the rank profile is only discovered during the algorithm, block size has then to be dynamic. We propose and compare several block decomposition: tile iterative with left-looking, right-looking and Crout variants, slab and tile recursive. Experiments demonstrate that the tile recursive variant performs better and matches the performance of reference numerical software when no rank deficiency occur. Furthermore, even in the most heterogeneous case, namely when all pivot blocks are rank deficient, we show that it is possbile to maintain a high efficiency.

Connected through hyperedge HE-1 (induced by source paper "lu factorization with errors"):
  with members:
    - "error correction in fast matrix multiplication and inverse"
    - "what can and can t we do with sparse polynomials"
    - "certificates for triangular equivalence and rank profiles"
    - "linear time interactive certificates for the minimal polynomial and the determinant of a sparse matrix"
    - "efficiently correcting matrix products"
    - "essentially optimal interactive certificates in linear algebra"
    - "powers of tensors and fast matrix multiplication"
    - "simultaneous computation of the row and column rank profiles"

Connected through hyperedge HE-2 (induced by source paper "fast computation of the rank profile matrix and the generalized bruhat decomposition"):
  with members:
    - "computing the rank profile matrix"
    - "generalized bruhat decomposition in commutative domains"
    - "simultaneous computation of the row and column rank profiles"
    - "fast matrix rank algorithms and applications"
    - "rank profile revealing gaussian elimination and the cup matrix decomposition"
    - "fast generalized bruhat decomposition"

Connected through hyperedge HE-3 (induced by source paper "gbla gr obner basis linear algebra package"):
  with members:
    - "elements of design for containers and solutions in the linbox library"

Connected through hyperedge HE-4 (induced by source paper "computing the rank profile matrix"):
  with members:
    - "simultaneous computation of the row and column rank profiles"
    - "rank profile revealing gaussian elimination and the cup matrix decomposition"
    - "fast generalized bruhat decomposition"

Connected through hyperedge HE-5 (induced by source paper "elements of design for containers and solutions in the linbox library"):
  with members:
    - "faster arithmetic for number theoretic transforms"
    - "linbox founding scope allocation parallel building blocks and separate compilation"
    - "exact sparse matrix vector multiplication on gpu s and multicore architectures"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"

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.SC(Symbolic Computation)