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: "pushing the bounds for matrix matrix multiplication"
  Abstract: A tight lower bound for required I/O when computing a matrix-matrix multiplication on a processor with two layers of memory is established. Prior work obtained weaker lower bounds by reasoning about the number of \textit{phases} needed to perform $C:=AB$, where each phase is a series of operations involving $S$ reads and writes to and from fast memory, and $S$ is the size of fast memory. A lower bound on the number of phases was then determined by obtaining an upper bound on the number of scalar multiplications performed per phase. This paper follows the same high level approach, but improves the lower bound by considering $C:=AB+C$ instead of $C:=AB$, and obtains the maximum number of scalar fused multiply-adds (FMAs) per phase instead of scalar additions. Key to obtaining the new result is the decoupling of the per-phase I/O from the size of fast memory. The new lower bound is $2mnk/\sqrt{S}-2S$. The constant for the leading term is an improvement of a factor $4\sqrt{2}$. A theoretical algorithm that attains the lower bound is given, and how the state-of-the-art Goto's algorithm also in some sense meets the lower bound is discussed.

Connected through hyperedge HE-1 (induced by source paper "red blue pebbling revisited near optimal parallel matrix matrix multiplication"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"
    - "i o efficient algorithms for matrix computations"

Connected through hyperedge HE-2 (induced by source paper "automated derivation of parametric data movement lower bounds for affine programs"):
  with members:
    - "on characterizing the data movement complexity of computational dags for parallel execution"
    - "minimizing communication in linear algebra"
    - "revisiting matrix product on master worker platforms"
    - "introduction to the ginac framework for symbolic computation within the c programming language"

Connected through hyperedge HE-3 (induced by source paper "the momms family of matrix multiplication algorithms"):
  with members:
    - "a tight i o lower bound for matrix multiplication"
    - "blasfeo basic linear algebra subroutines for embedded optimization"
    - "revisiting matrix product on master worker platforms"

Connected through hyperedge HE-4 (induced by source paper "communication lower bounds for matricized tensor times khatri rao product"):
  with members:
    - "shared memory parallelization of mttkrp for dense tensors"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "parallel tensor compression for large scale scientific data"
    - "tensor decompositions for learning latent variable models"

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)