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: "matchpy a pattern matching library"
  Abstract: Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstract syntax trees, and XML and JSON data. Unfortunately, no lightweight implementation of pattern matching as general and flexible as Mathematica exists for Python Mathics,MacroPy,patterns,PyPatt. Therefore, we created the open source module MatchPy which offers similar pattern matching functionality in Python using a novel algorithm which finds matches for large pattern sets more efficiently by exploiting similarities between patterns.

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"
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "efficient pattern matching in python"
    - "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"
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "efficient pattern matching in python"
    - "image restoration by iterative denoising and backward projections"
    - "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 "efficient pattern matching in python"):
  with members:
    - "non linear associative commutative many to one pattern matching with sequence variables"

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.PL(Programming Languages)