human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a graphblas approach for subgraph counting"):
  Abstract: Subgraph counting aims to count the occurrences of a subgraph template T in a given network G. The basic problem of computing structural properties such as counting triangles and other subgraphs has found applications in diverse domains. Recent biological, social, cybersecurity and sensor network applications have motivated solving such problems on massive networks with billions of vertices. The larger subgraph problem is known to be memory bounded and computationally challenging to scale; the complexity grows both as a function of T and G. In this paper, we study the non-induced tree subgraph counting problem, propose a novel layered softwarehardware co-design approach, and implement a shared-memory multi-threaded algorithm: 1) reducing the complexity of the parallel color-coding algorithm by identifying and pruning redundant graph traversal; 2) achieving a fully-vectorized implementation upon linear algebra kernels inspired by GraphBLAS, which significantly improves cache usage and maximizes memory bandwidth utilization. Experiments show that our implementation improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x for subgraph templates with size over 12 on a dual-socket Intel(R) Xeon(R) Platinum 8160 server. We believe our approach using GraphBLAS with optimized sparse linear algebra can be applied to other massive subgraph counting problems and emerging high-memory bandwidth hardware architectures.

Includes members:
  Member "design principles for sparse matrix multiplication on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast sparse convnets")
  Member "high performance massive subgraph counting using pipelined adaptive group communication":
    also connected through other hyperedges:
      (none)
  Member "accelerating pagerank using partition centric processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions")
  Member "subgraph counting color coding beyond trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motivo fast motif counting via succinct color coding and adaptive sampling")
  Member "the reverse cuthill mckee algorithm in distributed memory":
    also connected through other hyperedges:
      (none)
  Member "mathematical foundations of the graphblas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "d4m 3 0 extended database and language capabilities")
  Member "ringo interactive graph analytics on big memory machines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive low level storage of very large knowledge graphs")

Question: Please tell me which class the source paper 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)