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: "non linear associative commutative many to one pattern matching with sequence variables"
  Abstract: Pattern matching is a powerful tool which is part of many functional programming languages as well as computer algebra systems such as Mathematica. Among the existing systems, Mathematica offers the most expressive pattern matching. Unfortunately, no open source alternative has comparable pattern matching capabilities. Notably, these features include support for associative and/or commutative function symbols and sequence variables. While those features have individually been subject of previous research, their comprehensive combination has not yet been investigated. Furthermore, in many applications, a fixed set of patterns is matched repeatedly against different subjects. This many-to-one matching can be sped up by exploiting similarities between patterns. Discrimination nets are the state-of-the-art solution for many-to-one matching. In this thesis, a generalized discrimination net which supports the full feature set is presented. All algorithms have been implemented as an open-source library for Python. In experiments on real world examples, significant speedups of many-to-one over one-to-one matching have been observed.

Connected through hyperedge HE-1 (induced by source paper "linnea automatic generation of efficient linear algebra programs"):
  with members:
    - "the linear algebra mapping problem"
    - "automatic generation of efficient linear algebra programs"
    - "the generalized matrix chain algorithm"
    - "efficient pattern matching in python"
    - "matchpy a pattern matching library"
    - "sparsity based correction of exponential artifacts"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
    - "on a natural dynamics for linear programming"

Connected through hyperedge HE-2 (induced by source paper "automatic generation of efficient linear algebra programs"):
  with members:
    - "the generalized matrix chain algorithm"
    - "efficient pattern matching in python"
    - "image restoration by iterative denoising and backward projections"
    - "matchpy a pattern matching library"
    - "sparsity based correction of exponential artifacts"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
    - "on a natural dynamics for linear programming"
    - "a study on the influence of caching sequences of dense linear algebra kernels"

Connected through hyperedge HE-3 (induced by source paper "the generalized matrix chain algorithm"):
  with members:
    - "deriving correct high performance algorithms"
    - "a dual method for computing power transfer distribution factors"
    - "sparsity based correction of exponential artifacts"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
    - "consistent discretization and minimization of the l1 norm on manifolds"
    - "the elaps framework experimental linear algebra performance studies"
    - "on a natural dynamics for linear programming"
    - "a study on the influence of caching sequences of dense linear algebra kernels"

Connected through hyperedge HE-4 (induced by source paper "non linear pattern matching with backtracking for non free data types"):
  with members:
    - "scalar and tensor parameters for importing the notation in differential geometry into programming"
    - "scalar and tensor parameters for importing tensor index notation including einstein summation notation"
    - "non linear pattern matching against unfree data types with lexical scoping"

Connected through hyperedge HE-5 (induced by source paper "efficient pattern matching in python"):
  with members:
    - "matchpy a pattern matching library"

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)