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: "worst case optimal join algorithms"
  Abstract: Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a novel algorithm to process these queries optimally in terms of worst-case data complexity. Our result builds on recent work by Atserias, Grohe, and Marx, who gave bounds on the size of a full conjunctive query in terms of the sizes of the individual relations in the body of the query. These bounds, however, are not constructive: they rely on Shearer's entropy inequality which is information-theoretic. Thus, the previous results leave open the question of whether there exist algorithms whose running time achieve these optimal bounds. An answer to this question may be interesting to database practice, as it is known that any algorithm based on the traditional select-project-join style plans typically employed in an RDBMS are asymptotically slower than the optimal for some queries. We construct an algorithm whose running time is worst-case optimal for all natural join queries. Our result may be of independent interest, as our algorithm also yields a constructive proof of the general fractional cover bound by Atserias, Grohe, and Marx without using Shearer's inequality. This bound implies two famous inequalities in geometry: the Loomis-Whitney inequality and the Bollob\'as-Thomason inequality. Hence, our results algorithmically prove these inequalities as well. Finally, we discuss how our algorithm can be used to compute a relaxed notion of joins.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "size bounds and query plans for relational joins"
    - "covers of query results"
    - "constraint solving via fractional edge covers"
    - "answering conjunctive queries under updates"

Connected through hyperedge HE-3 (induced by source paper "parallel algorithms for butterfly computations"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "measuring and modeling bipartite graphs with community structure"
    - "graphlet decomposition framework algorithms and applications"

Connected through hyperedge HE-4 (induced by source paper "instance and output optimal parallel algorithms for acyclic joins"):
  with members:
    - "size bounds and query plans for relational joins"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "skinnerdb regret bounded query evaluation via reinforcement learning"):
  with members:
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "sampling based query re optimization"
    - "ordering selection operators using the minmax regret rule"
    - "bandit algorithms for tree search"
    - "simple regret optimization in online planning for markov decision processes"
    - "robust estimation of resource consumption for sql queries using statistical techniques"
    - "leapfrog triejoin a worst case optimal join algorithm"

Connected through hyperedge HE-6 (induced by source paper "box covers and domain orderings for beyond worst case join processing"):
  with members:
    - "worst case optimal join algorithms techniques results and open problems"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "computing join queries with functional dependencies"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "joins via geometric resolutions worst case and beyond"
    - "beyond worst case analysis for joins with minesweeper"

Connected through hyperedge HE-7 (induced by source paper "a survey and experimental analysis of distributed subgraph matching"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "communication steps for parallel query processing"
    - "enumerating subgraph instances using map reduce"
    - "efficient subgraph matching on billion node graphs"

Connected through hyperedge HE-8 (induced by source paper "optimizing subgraph queries by combining binary and worst case optimal joins"):
  with members:
    - "size bounds and query plans for relational joins"
    - "join processing for graph patterns an old dog with new tricks"
    - "skew strikes back new developments in the theory of join algorithms"
    - "leapfrog triejoin a worst case optimal join algorithm"

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)