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: "a new general purpose method to multiply 3x3 matrices using only 23 multiplications"
  Abstract: One of the most famous conjectures in computer algebra is that matrix multiplication might be feasible in nearly quadratic time, (8). The best known exponent is 2.376, due to Coppersmith and Winograd (9). Many attempts to solve this problems in the literature work by solving, fixed-size problems and then apply the solution recursively (6,22,17,21,2). This leads to pure combinatorial optimisation problems with fixed size. These problems are unlikely to be solvable in polynomial time, see (21,15). In 1976 Laderman published a method to multiply two 3x3 matrices using only 23 multiplications. This result is non-commutative, and therefore can be applied recursively to smaller sub-matrices. In 35 years nobody was able to do better and it remains an open problem if this can be done with 22 multiplications. We proceed by solving the so called Brent equations (6). We have im- plemented a method to converting this very hard problem to a SAT problem, and we have attempted to solve it, with our portfolio of some 500 SAT solvers. With this new method we were able to produce new solutions to the Laderman's problem. We present a new fully general non-commutative solution with 23 multi- plications and show that this solution is new and is NOT equivalent to any previously known solution. This result demonstrates that the space of solutions to Laderman's problem is larger than expected, and therefore it becomes now more plausible that a solution with 22 multiplications exists. If it exists, we might be able to find it soon just by running our algorithms longer, or due to further improvements in the SAT solver algorithms.

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"
    - "fast feasible and unfeasible 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"

Connected through hyperedge HE-2 (induced by source paper "local search for fast matrix multiplication"):
  with members:
    - "fast feasible and unfeasible matrix multiplication"

Connected through hyperedge HE-3 (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"
    - "fast matrix multiplication limitations of the laser method"

Connected through hyperedge HE-4 (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"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"
    - "on symmetries of the strassen algorithm"
    - "equations for lower bounds on border rank"

Connected through hyperedge HE-5 (induced by source paper "a framework for practical parallel fast matrix multiplication"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
    - "communication optimal parallel algorithm for strassen s 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.SC(Symbolic Computation)