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 feasible and unfeasible matrix multiplication"
  Abstract: Fast matrix-by-matrix multiplication (hereafter MM) is a highly recognized research subject. The record upper bound 3 of 1968 on the exponent of the complexity MM decreased below 2.38 by 1987, applies to celebrated problems in many areas of computing, and is extensively cited in the Theory of Computing. Further decrease of the exponent remains a celebrated challenge. Acceleration of MM in the Practice of Computing is a distinct challenge, because all known algorithms supporting the exponents below 2.7733 improve straightforward MM only for unfeasible MM of immense size, greatly exceeding the sizes of interest nowadays and in any foreseeable future. We first survey the mainstream study of the acceleration of MM of unbounded sizes, cover the progress in decreasing the exponents of MM, comment on its impact on the theory and practice of computing, and recall various fundamental concepts and techniques supporting fast MM and naturally introduced in that study by 1980. Then we demonstrate how the curse of recursion naturally entered the game of decreasing the record exponents. Finally we cover the State of the Art of efficient feasible MM, including some most efficient known techniques and algorithms as well as various issues of numerical and symbolic implementation. We hope that our review will help motivate and properly focus further effort in this highly important area.

Connected through hyperedge HE-1 (induced by source paper "new ways to multiply 3 x 3 matrices"):
  with members:
    - "local search for fast matrix multiplication"
    - "laderman matrix multiplication algorithm can be constructed using strassen algorithm and related tensor s isotropies"
    - "a non commutative algorithm for multiplying 5 5 matrices using 99 multiplications"
    - "schur number five"
    - "solving and verifying the boolean pythagorean triples problem via cube and conquer"
    - "powers of tensors and fast matrix multiplication"
    - "a new general purpose method to multiply 3x3 matrices using only 23 multiplications"

Connected through hyperedge HE-2 (induced by source paper "engineering boolean matrix multiplication for multiple accelerator shared memory architectures"):
  with members:
    - "generating families of practical fast matrix multiplication algorithms"
    - "dissecting gpu memory hierarchy through microbenchmarking"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "local search for fast matrix multiplication"):
  with members:
    - "a new general purpose method to multiply 3x3 matrices using only 23 multiplications"

Connected through hyperedge HE-4 (induced by source paper "a new algorithm for finding closest pair of vectors"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "distributed pcp theorems for hardness of approximation in p"
    - "generating families of practical fast matrix multiplication algorithms"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "a framework for practical parallel fast 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.DS(Data Structures and Algorithms)