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: "size bounds and query plans for relational joins"
  Abstract: Relational joins are at the core of relational algebra, which in turn is the core of the standard database query language SQL. As their evaluation is expensive and very often dominated by the output size, it is an important task for database query optimisers to compute estimates on the size of joins and to find good execution plans for sequences of joins. We study these problems from a theoretical perspective, both in the worst-case model, and in an average-case model where the database is chosen according to a known probability distribution. In the former case, our first key observation is that the worst-case size of a query is characterised by the fractional edge cover number of its underlying hypergraph, a combinatorial parameter previously known to provide an upper bound. We complete the picture by proving a matching lower bound, and by showing that there exist queries for which the join-project plan suggested by the fractional edge cover approach may be substantially better than any join plan that does not use intermediate projections. On the other hand, we show that in the average-case model, every join-project plan can be turned into a plan containing no projections in such a way that the expected time to evaluate the plan increases only by a constant factor independent of the size of the database. Not surprisingly, the key combinatorial parameter in this context is the maximum density of the underlying hypergraph. We show how to make effective use of this parameter to eliminate the projections.

Connected through hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "myhill nerode methods for hypergraphs"
    - "faq questions asked frequently"

Connected through hyperedge HE-2 (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"
    - "extracting and analyzing hidden graphs from relational databases"
    - "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-3 (induced by source paper "learning models over relational data a brief tutorial"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "incremental techniques for large scale dynamic query processing"
    - "counting triangles under updates in worst case optimal time"
    - "on functional aggregate queries with additive inequalities"
    - "incremental view maintenance with triple lock factorization benefits"
    - "constraint solving via fractional edge covers"
    - "boolean tensor decomposition for conjunctive queries with negation"
    - "building efficient query engines in a high level language"

Connected through hyperedge HE-4 (induced by source paper "optimal algorithms for ranked enumeration of answers to full conjunctive queries"):
  with members:
    - "ranked enumeration of conjunctive query results"
    - "any k anytime top k tree pattern retrieval in labeled graphs"
    - "on functional aggregate queries with additive inequalities"
    - "on the enumeration complexity of unions of conjunctive queries"
    - "covers of query results"
    - "constraint solving via fractional edge covers"
    - "answering conjunctive queries under updates"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"

Connected through hyperedge HE-5 (induced by source paper "instance and output optimal parallel algorithms for acyclic joins"):
  with members:
    - "constraint solving via fractional edge covers"
    - "answering conjunctive queries under updates"
    - "worst case optimal algorithms for parallel query processing"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "the input output complexity of triangle enumeration"
    - "skew in parallel query processing"
    - "gym a multiround join algorithm in mapreduce"

Connected through hyperedge HE-6 (induced by source paper "optimal joins using compact data structures"):
  with members:
    - "worst case optimal join algorithms techniques results and open problems"
    - "compressed representation of dynamic binary relations with applications"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "faster compressed quadtrees"
    - "beyond worst case analysis for joins with minesweeper"

Connected through hyperedge HE-7 (induced by source paper "a join based hybrid parameter for constraint satisfaction"):
  with members:
    - "binary constraint satisfaction problems defined by excluded topological minors"
    - "constraint solving via fractional edge covers"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "hypertree decompositions and tractable queries"
    - "using pivot consistency to decompose and solve functional csps"

Connected through hyperedge HE-8 (induced by source paper "ranked enumeration of conjunctive query results"):
  with members:
    - "any k anytime top k tree pattern retrieval in labeled graphs"
    - "answering fo mod queries under updates on bounded degree databases"
    - "answering conjunctive queries under updates"
    - "compressed representations of conjunctive query results"
    - "skew strikes back new developments in the theory of join algorithms"
    - "fdb a query engine for factorised relational databases"
    - "optimal aggregation algorithms for middleware"

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)