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: "extracting and analyzing hidden graphs from relational databases"
  Abstract: Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics can provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to manually extract data from their data stores, construct the requisite graphs, and then load them into some graph engine in order to execute their graph analysis task. Moreover, in many cases (especially when the graphs are dense), these graphs can be significantly larger than the initial input stored in the database, making it infeasible to construct or analyze such graphs in memory. In this paper we address both of these challenges by building a system that enables users to declaratively specify graph extraction tasks over a relational database schema and then execute graph algorithms on the extracted graphs. We propose a declarative domain specific language for this purpose, and pair it up with a novel condensed, in-memory representation that significantly reduces the memory footprint of these graphs, permitting analysis of larger-than-memory graphs. We present a general algorithm for creating such a condensed representation for a large class of graph extraction queries against arbitrary schemas. We observe that the condensed representation suffers from a duplication issue, that results in inaccuracies for most graph algorithms. We then present a suite of in-memory representations that handle this duplication in different ways and allow trading off the memory required and the computational cost for executing different graph algorithms. We also introduce several novel deduplication algorithms for removing this duplication in the graph, which are of independent interest for graph compression, and provide a comprehensive experimental evaluation over several real-world and synthetic datasets illustrating these trade-offs.

Connected through hyperedge HE-1 (induced by source paper "fast join project query evaluation using matrix multiplication"):
  with members:
    - "memory efficient group by aggregates over multi way joins"
    - "trade offs in static and dynamic evaluation of hierarchical queries"
    - "size bounds and query plans for relational joins"
    - "compressed representations of conjunctive query results"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "set containment join revisited"
    - "data structure lower bounds for document indexing problems"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-2 (induced by source paper "kaskade graph views for efficient graph analytics"):
  with members:
    - "graph summarization methods and applications a survey"
    - "view selection in semantic web databases"
    - "graphlab a new framework for parallel machine learning"
    - "efficient and extensible algorithms for multi query optimization"

Connected through hyperedge HE-3 (induced by source paper "compressed representations of conjunctive query results"):
  with members:
    - "size bounds and query plans for relational joins"
    - "conditional lower bounds for space time tradeoffs"
    - "answering conjunctive queries under updates"
    - "data structure lower bounds for document indexing problems"
    - "aggregation and ordering in factorised databases"
    - "structural tractability of counting of solutions to conjunctive queries"
    - "skew strikes back new developments in the theory of join algorithms"
    - "fdb a query engine for factorised relational databases"

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)