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: "lara a key value algebra underlying arrays and relations"
  Abstract: Data processing systems roughly group into families such as relational, array, graph, and key-value. Many data processing tasks exceed the capabilities of any one family, require data stored across families, or run faster when partitioned onto multiple families. Discovering ways to execute computation among multiple available systems, let alone discovering an optimal execution plan, is challenging given semantic differences between disparate families of systems. In this paper we introduce a new algebra, Lara, which underlies and unifies algebras representing the families above in order to facilitate translation between systems. We describe the operations and objects of Lara---union, join, and ext on associative tables---and show her properties and equivalences to other algebras. Multi-system optimization has a bright future, in which we proffer Lara for the role of universal connector.

Connected through hyperedge HE-1 (induced by source paper "towards scalable dataframe systems"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "ray a distributed framework for emerging ai applications"
    - "adaptive sampling for rapidly matching histograms"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "towards a holistic integration of spreadsheets with databases a scalable storage engine for presentational data management"
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"

Connected through hyperedge HE-2 (induced by source paper "laradb a minimalist kernel for linear and relational algebra computation"):
  with members:
    - "associative array model of sql nosql and newsql databases"
    - "mathematical foundations of the graphblas"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "using a power law distribution to describe big data"
    - "mllib machine learning in apache spark"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "big data dimensional analysis"

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)