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: "system g distributed graph database"
  Abstract: Motivated by the need to extract knowledge and value frominterconnected data, graph analytics on big data is a veryactive area of research in both industry and academia. Tosupport graph analytics efficiently a large number of in mem-ory graph libraries, graph processing systems and graphdatabases have emerged. Projects in each of these cate-gories focus on particular aspects such as static versus dy-namic graphs, off line versus on line processing, small versuslarge graphs, etc.While there has been much advance in graph processingin the past decades, there is still a need for a fast graph pro-cessing, using a cluster of machines with distributed storage.In this paper, we discuss a novel distributed graph databasecalled System G designed for efficient graph data storage andprocessing on modern computing architectures. In particu-lar we describe a single node graph database and a runtimeand communication layer that allows us to compose a dis-tributed graph database from multiple single node instances.From various industry requirements, we find that fast inser-tions and large volume concurrent queries are critical partsof the graph databases and we optimize our database forsuch features. We experimentally show the efficiency ofSystem G for storing data and processing graph queries onstate-of-the-art platforms.

Connected through hyperedge HE-1 (induced by source paper "characterizing entities in the bitcoin blockchain"):
  with members:
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "ransomware payments in the bitcoin ecosystem"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "when a small leak sinks a great ship deanonymizing tor hidden service users through bitcoin transactions analysis"
    - "blockchain a graph primer"
    - "the unreasonable effectiveness of address clustering"
    - "link prediction by de anonymization how we won the kaggle social network challenge"

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)