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: "tspdb time series predict db"
  Abstract: In this work, we are motivated to make predictive functionality native to database (DB) systems, with a focus on time series data. We propose a system architecture, time series predict DB (tspDB), that enables predictive query functionality in any existing relational DB by building an additional prediction index for a time series of interest. Like standard DB indices, a prediction index must allow for fast data retrieval, but for entries that: (i) are for a future time step (i.e. forecasting); (ii) are missing/corrupted by noise (i.e. imputation). Such an index must satisfy: (i) statistical accuracy; (ii) high DB throughput; (iii) low predictive query latency; (iv) flexibility across DB/machines. Our hope is tspDB allows any analyst who can make a SQL query to make a predictive SQL query without loss of system performance, thereby increasing accessibility of machine learning. We build such a prediction index in PostgreSQL. it uses a recently developed prediction algorithm, which we alter to be incremental while maintaining its statistical accuracy. As an important algorithmic contribution, we develop a novel algorithm to estimate time-varying noise variance to enable uncertainty quantification. We argue it provides consistent estimation for a rich class of generating processes. Despite extensive literature, our variance estimation algorithm and associated analysis is first of its kind. For tspDB to function as a real-time system, we measure performance not only statistically but also through standard DB metrics such as latency/throughput. Statistically, the prediction index provides better accuracy compared to the state-of-art libraries. In terms of throughput, our index updates 2.83x faster than PostgreSQL bulk insert. Finally, predictive query latency with respect to standard SELECT queries is < 2.53x, < 6.09x for imputation/forecasting respectively (across DBs/machines)

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)