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: "hypergraph partitioning for sparse matrix matrix multiplication"
  Abstract: We propose a fine-grained hypergraph model for sparse matrix-matrix multiplication (SpGEMM), a key computational kernel in scientific computing and data analysis whose performance is often communication bound. This model correctly describes both the interprocessor communication volume along a critical path in a parallel computation and also the volume of data moving through the memory hierarchy in a sequential computation. We show that identifying a communication-optimal algorithm for particular input matrices is equivalent to solving a hypergraph partitioning problem. Our approach is sparsity dependent, meaning that we seek the best algorithm for the given input matrices. #R##N#In addition to our (3D) fine-grained model, we also propose coarse-grained 1D and 2D models that correspond to simpler SpGEMM algorithms. We explore the relations between our models theoretically, and we study their performance experimentally in the context of three applications that use SpGEMM as a key computation. For each application, we find that at least one coarse-grained model is as communication efficient as the fine-grained model. We also observe that different applications have affinities for different algorithms. #R##N#Our results demonstrate that hypergraphs are an accurate model for reasoning about the communication costs of SpGEMM as well as a practical tool for exploring the SpGEMM algorithm design space.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "high performance sparse matrix matrix products on intel knl and multicore architectures"
    - "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
    - "a survey of sparse matrix vector multiplication performance on large matrices"

Connected through hyperedge HE-2 (induced by source paper "hypergraph cuts with general splitting functions"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "three hypergraph eigenvector centralities"
    - "simplicial closure and higher order link prediction"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "higher order organization of complex networks"
    - "a simple and strongly local flow based method for cut improvement"
    - "simple and fast rounding algorithms for directed and node weighted multiway cut"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"

Connected through hyperedge HE-3 (induced by source paper "parallel memory efficient all at once algorithms for the sparse matrix triple products in multigrid methods"):
  with members:
    - "a general purpose hierarchical mesh partitioning method with node balancing strategies for large scale numerical simulations"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-4 (induced by source paper "sparse matrix multiplication with bandwidth restricted all to all communication"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "mst in o 1 rounds of the congested clique"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "towards a complexity theory for the congested clique"
    - "tight bounds for distributed graph computations"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "derandomizing local distributed algorithms under bandwidth restrictions"

Connected through hyperedge HE-5 (induced by source paper "sparse matrix multiplication and triangle listing in the congested clique model"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "triangle finding and listing in congest networks"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "on the distributed complexity of large scale graph computations"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "fast feasible and unfeasible matrix multiplication"):
  with members:
    - "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"
    - "fast matrix multiplication limitations of the laser method"
    - "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-7 (induced by source paper "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "semi external memory sparse matrix multiplication for billion node graphs"
    - "early evaluation of intel optane non volatile memory with hpc i o workloads"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-8 (induced by source paper "a 3d parallel algorithm for qr decomposition"):
  with members:
    - "communication avoiding parallel algorithms for solving triangular systems of linear equations"
    - "a communication avoiding parallel algorithm for the symmetric eigenvalue problem"

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.DC(Distributed, Parallel, and Cluster Computing)