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: "fast matrix multiplication limitations of the laser method"
  Abstract: Until a few years ago, the fastest known matrix multiplication algorithm, due to Coppersmith and Winograd (1990), ran in time $O(n^{2.3755})$. Recently, a surge of activity by Stothers, Vassilevska-Williams, and Le Gall has led to an improved algorithm running in time $O(n^{2.3729})$. These algorithms are obtained by analyzing higher and higher tensor powers of a certain identity of Coppersmith and Winograd. We show that this exact approach cannot result in an algorithm with running time $O(n^{2.3725})$, and identify a wide class of variants of this approach which cannot result in an algorithm with running time $O(n^{2.3078})$; in particular, this approach cannot prove the conjecture that for every $\epsilon > 0$, two $n\times n$ matrices can be multiplied in time $O(n^{2+\epsilon})$. #R##N#We describe a new framework extending the original laser method, which is the method underlying the previously mentioned algorithms. Our framework accommodates the algorithms by Coppersmith and Winograd, Stothers, Vassilevska-Williams and Le Gall. We obtain our main result by analyzing this framework. The framework is also the first to explain why taking tensor powers of the Coppersmith-Winograd identity results in faster algorithms.

Connected through hyperedge HE-1 (induced by source paper "barriers for rectangular matrix multiplication"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "limits on the universal method for matrix multiplication"
    - "barriers for fast matrix multiplication from irreversibility"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "further limitations of the known approaches for matrix multiplication"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "geometric rank of tensors and subrank of matrix multiplication"):
  with members:
    - "variety membership testing algebraic natural proofs and geometric complexity theory"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "on multilinear forms bias correlation and tensor rank"
    - "limits on the universal method for matrix multiplication"
    - "barriers for fast matrix multiplication from irreversibility"
    - "bias vs structure of polynomials in large fields and applications in effective algebraic geometry and coding theory"
    - "powers of tensors and fast matrix multiplication"
    - "variety evasive sets"

Connected through hyperedge HE-3 (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"
    - "powers of tensors and fast matrix multiplication"
    - "nearly optimal computations with structured matrices"
    - "better late than never filling a void in the history of fast matrix multiplication and tensor decompositions"

Connected through hyperedge HE-4 (induced by source paper "laderman matrix multiplication algorithm can be constructed using strassen algorithm and related tensor s isotropies"):
  with members:
    - "the geometry of rank decompositions of matrix multiplication i 2x2 matrices"
    - "matrix multiplication algorithms from group orbits"
    - "fast matrix multiplication and symbolic computation"
    - "symmetries of matrix multiplication algorithms i"
    - "a new general purpose method to multiply 3x3 matrices using only 23 multiplications"

Connected through hyperedge HE-5 (induced by source paper "matrix multiplication algorithms from group orbits"):
  with members:
    - "the geometry of rank decompositions of matrix multiplication i 2x2 matrices"
    - "a 2n 2 log n 1 lower bound for the border rank of matrix multiplication"
    - "on the geometry of border rank algorithms for n x 2 by 2 x 2 matrix multiplication"
    - "symmetries of matrix multiplication algorithms i"
    - "powers of tensors and fast matrix multiplication"
    - "on symmetries of the strassen algorithm"
    - "equations for lower bounds on border rank"
    - "new lower bounds for the rank of matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "fast matrix multiplication and symbolic computation"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "improving the numerical stability of fast matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "elements of design for containers and solutions in the linbox library"
    - "bitslicing and the method of four russians over larger finite fields"

Connected through hyperedge HE-7 (induced by source paper "matrix multiplication trilinear decompositions apa algorithms and summation"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "better late than never filling a void in the history of fast matrix multiplication and tensor decompositions"
    - "faster algorithms for rectangular matrix multiplication"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

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.CC(Computational Complexity)