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: "flexible caching in trie joins"
  Abstract: Traditional algorithms for multiway join computation are based on rewriting the order of joins and combining results of intermediate subqueries. Recently, several approaches have been proposed for algorithms that are "worst-case optimal" wherein all relations are scanned simultaneously. An example is Veldhuizen's Leapfrog Trie Join (LFTJ). An important advantage of LFTJ is its small memory footprint, due to the fact that intermediate results are full tuples that can be dumped immediately. However, since the algorithm does not store intermediate results, recurring joins must be reconstructed from the source relations, resulting in excessive memory traffic. In this paper, we address this problem by incorporating caches into LFTJ. We do so by adopting recent developments on join optimization, tying variable ordering to tree decomposition. While the traditional usage of tree decomposition computes the result for each bag in advance, our proposed approach incorporates caching directly into LFTJ and can dynamically adjust the size of the cache. Consequently, our solution balances memory usage and repeated computation, as confirmed by our experiments over SNAP datasets.

Connected through hyperedge HE-1 (induced by source paper "the triejax architecture accelerating graph operations through relational joins"):
  with members:
    - "size bounds and query plans for relational joins"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-2 (induced by source paper "juggling functions inside a database"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-3 (induced by source paper "an analytical study of large sparql query logs"):
  with members:
    - "gmark schema driven generation of graphs and queries"
    - "enumeration problems for regular path queries"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "on the statistical analysis of practical sparql queries"
    - "a trichotomy for regular simple path queries on graphs"
    - "an empirical study of real world sparql queries"
    - "semantics and complexity of sparql"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-4 (induced by source paper "ranked enumeration of minimal triangulations"):
  with members:
    - "constraint solving via fractional edge covers"
    - "on distance d independent set and other problems in graphs with few minimal separators"
    - "reduction techniques for graph isomorphism in the context of width parameters"
    - "large induced subgraphs via triangulations and cmso"
    - "pure nash equilibria hard and easy games"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-5 (induced by source paper "greedy strategies and larger islands of tractability for conjunctive queries and constraint satisfaction problems"):
  with members:
    - "constraint solving via fractional edge covers"
    - "gym a multiround join algorithm in mapreduce"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "tree projections and structural decomposition methods minimality and game theoretic characterization"
    - "on the computational complexity of a game of cops and robbers"
    - "hybrid tractability of valued constraint problems"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "hypertree decompositions and tractable queries"

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)