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: "parallel breadth first search on distributed memory systems"
  Abstract: Data-intensive, graph-based computations are pervasive in several scientific applications, and are known to to be quite challenging to implement on distributed memory systems. In this work, we explore the design space of parallel algorithms for Breadth-First Search (BFS), a key subroutine in several graph algorithms. We present two highly-tuned parallel approaches for BFS on large parallel systems: a level-synchronous strategy that relies on a simple vertex-based partitioning of the graph, and a two-dimensional sparse matrix-partitioning-based approach that mitigates parallel communication overhead. For both approaches, we also present hybrid versions with intra-node multithreading. Our novel hybrid two-dimensional algorithm reduces communication times by up to a factor of 3.5, relative to a common vertex based approach. Our experimental study identifies execution regimes in which these approaches will be competitive, and we demonstrate extremely high performance on leading distributed-memory parallel systems. For instance, for a 40,000-core parallel execution on Hopper, an AMD Magny-Cours based system, we achieve a BFS performance rate of 17.8 billion edge visits per second on an undirected graph of 4.3 billion vertices and 68.7 billion edges with skewed degree distribution.

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"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"

Connected through hyperedge HE-2 (induced by source paper "fastsv a distributed memory connected component algorithm with fast convergence"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "composing optimization techniques for vertex centric graph processing via communication channels"
    - "an adaptive parallel algorithm for computing connected components"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "finding connected components on map reduce in logarithmic rounds"

Connected through hyperedge HE-3 (induced by source paper "an adaptive load balancer for graph analytical applications on gpus"):
  with members:
    - "gunrock gpu graph analytics"
    - "dynamic load balancing strategies for graph applications on gpus"
    - "multi gpu graph analytics"
    - "gunrock a high performance graph processing library on the gpu"

Connected through hyperedge HE-4 (induced by source paper "optimal algebraic breadth first search for sparse graphs"):
  with members:
    - "implementing push pull efficiently in graphblas"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"

Connected through hyperedge HE-5 (induced by source paper "implementing push pull efficiently in graphblas"):
  with members:
    - "gunrock gpu graph analytics"
    - "the power of sparsity in convolutional neural networks"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"

Connected through hyperedge HE-6 (induced by source paper "an empirical evaluation of allgatherv on multi gpu systems"):
  with members:
    - "optimized broadcast for deep learning workloads on dense gpu infiniband clusters mpi or nccl"
    - "a unified optimization approach for sparse tensor operations on gpus"

Connected through hyperedge HE-7 (induced by source paper "a work efficient parallel sparse matrix sparse vector multiplication algorithm"):
  with members:
    - "mathematical foundations of the graphblas"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-8 (induced by source paper "the reverse cuthill mckee algorithm in distributed memory"):
  with members:
    - "distributed memory breadth first search on massive graphs"

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)