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: "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
  Abstract: Graph expansion analysis of computational DAGs is useful for obtaining communication cost lower bounds where previous methods, such as geometric embedding, are not applicable. This has recently been demonstrated for Strassen's and Strassen-like fast square matrix multiplication algorithms. Here we extend the expansion analysis approach to fast algorithms for rectangular matrix multiplication, obtaining a new class of communication cost lower bounds. These apply, for example to the algorithms of Bini et al. (1979) and the algorithms of Hopcroft and Kerr (1971). Some of our bounds are proved to be optimal.

Connected through hyperedge HE-1 (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"
    - "minimizing communication in linear algebra"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-2 (induced by source paper "fast feasible and unfeasible matrix multiplication"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "improving the numerical stability of fast matrix multiplication algorithms"
    - "a framework for practical parallel fast matrix multiplication"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"
    - "nearly optimal computations with structured matrices"

Connected through hyperedge HE-3 (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"
    - "faster algorithms for rectangular matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"
    - "minimizing communication in linear algebra"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-4 (induced by source paper "a framework for practical parallel fast matrix multiplication"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"
    - "a new general purpose method to multiply 3x3 matrices using only 23 multiplications"

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.DS(Data Structures and Algorithms)