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: "further limitations of the known approaches for matrix multiplication"
  Abstract: We consider the techniques behind the current best algorithms for matrix multiplication. Our results are threefold. #R##N#(1) We provide a unifying framework, showing that all known matrix multiplication running times since 1986 can be achieved from a single very natural tensor - the structural tensor $T_q$ of addition modulo an integer $q$. #R##N#(2) We show that if one applies a generalization of the known techniques (arbitrary zeroing out of tensor powers to obtain independent matrix products in order to use the asymptotic sum inequality of Sch\"{o}nhage) to an arbitrary monomial degeneration of $T_q$, then there is an explicit lower bound, depending on $q$, on the bound on the matrix multiplication exponent $\omega$ that one can achieve. We also show upper bounds on the value $\alpha$ that one can achieve, where $\alpha$ is such that $n\times n^\alpha \times n$ matrix multiplication can be computed in $n^{2+o(1)}$ time. #R##N#(3) We show that our lower bound on $\omega$ approaches $2$ as $q$ goes to infinity. This suggests a promising approach to improving the bound on $\omega$: for variable $q$, find a monomial degeneration of $T_q$ which, using the known techniques, produces an upper bound on $\omega$ as a function of $q$. Then, take $q$ to infinity. It is not ruled out, and hence possible, that one can obtain $\omega=2$ in this way.

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"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "limits on the universal method for matrix multiplication"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"

Connected through hyperedge HE-3 (induced by source paper "kronecker powers of tensors and strassen s laser method"):
  with members:
    - "barriers for fast matrix multiplication from irreversibility"
    - "barriers for rank methods in arithmetic complexity"
    - "geometric complexity theory and matrix powering"
    - "on degeneration of tensors and algebras"

Connected through hyperedge HE-4 (induced by source paper "limits on the universal method for matrix multiplication"):
  with members:
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "barriers for fast matrix multiplication from irreversibility"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "powers of tensors and fast matrix multiplication"
    - "faster algorithms for rectangular matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "the complexity of matrix multiplication developments since 2014 extended abstract of 2018 oberwolfach complexity meeting plenary lecture"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "equations for lower bounds on border rank"
    - "new lower bounds for the rank of matrix multiplication"
    - "new lower bounds for the border rank of matrix multiplication"
    - "tensor rank some lower and upper bounds"

Connected through hyperedge HE-6 (induced by source paper "limits on all known and some unknown approaches to matrix multiplication"):
  with members:
    - "powers of tensors and 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.CC(Computational Complexity)