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: "bigsparse high performance external graph analytics"
  Abstract: We present BigSparse, a fully external graph analytics system that picks up where semi-external systems like FlashGraph and X-Stream, which only store vertex data in memory, left off. BigSparse stores both edge and vertex data in an array of SSDs and avoids random updates to the vertex data, by first logging the vertex updates and then sorting the log to sequentialize accesses to the SSDs. This newly introduced sorting overhead is reduced significantly by interleaving sorting with vertex reduction operations. In our experiments on a server with 32GB to 64GB of DRAM, BigSparse outperforms other in-memory and semi-external graph analytics systems for algorithms such as PageRank, BreadthFirst Search, and Betweenness-Centrality for terabyte-size graphs with billions of vertices. BigSparse is capable of highspeed analytics of much larger graphs, on the same machine configuration.

Connected through hyperedge HE-1 (induced by source paper "single machine graph analytics on massive datasets using intel optane dc persistent memory"):
  with members:
    - "system evaluation of the intel optane byte addressable nvm"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "early evaluation of intel optane non volatile memory with hpc i o workloads"
    - "the gap benchmark suite"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-2 (induced by source paper "theoretically efficient parallel graph algorithms can be fast and scalable"):
  with members:
    - "single source bottleneck path algorithm faster than sorting for sparse graphs"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "parallelism in randomized incremental algorithms"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "nearly work efficient parallel algorithm for digraph reachability"
    - "a new efficient construction on probabilistic tree embeddings"
    - "compressing graphs and indexes with recursive graph bisection"
    - "the gap benchmark suite"

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.DB(Databases)