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: "leapfrog triejoin a worst case optimal join algorithm"
  Abstract: Recent years have seen exciting developments in join algorithms. In 2008, Atserias, Grohe and Marx (henceforth AGM) proved a tight bound on the maximum result size of a full conjunctive query, given constraints on the input relation sizes. In 2012, Ngo, Porat, R{\'e} and Rudra (henceforth NPRR) devised a join algorithm with worst-case running time proportional to the AGM bound. Our commercial Datalog system LogicBlox employs a novel join algorithm, \emph{leapfrog triejoin}, which compared conspicuously well to the NPRR algorithm in preliminary benchmarks. This spurred us to analyze the complexity of leapfrog triejoin. In this paper we establish that leapfrog triejoin is also worst-case optimal, up to a log factor, in the sense of NPRR. We improve on the results of NPRR by proving that leapfrog triejoin achieves worst-case optimality for finer-grained classes of database instances, such as those defined by constraints on projection cardinalities. We show that NPRR is \emph{not} worst-case optimal for such classes, giving a counterexample where leapfrog triejoin runs in $O(n \log n)$ time, compared to $\Theta(n^{1.375})$ time for NPRR. On a practical note, leapfrog triejoin can be implemented using conventional data structures such as B-trees, and extends naturally to $\exists_1$ queries. We believe our algorithm offers a useful addition to the existing toolbox of join algorithms, being easy to absorb, simple to implement, and having a concise optimality proof.

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 "memory efficient group by aggregates over multi way joins"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "on functional aggregate queries with additive inequalities"
    - "covers of query results"
    - "boolean tensor decomposition for conjunctive queries with negation"
    - "worst case optimal algorithms for parallel query processing"
    - "aggregations over generalized hypertree decompositions"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-3 (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"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-4 (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"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-5 (induced by source paper "efficient multiway hash join on reconfigurable hardware"):
  with members:
    - "reconfigurable hardware accelerators opportunities trends and challenges"

Connected through hyperedge HE-6 (induced by source paper "distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows"):
  with members:
    - "size bounds and query plans for relational joins"
    - "worst case optimal algorithms for parallel query processing"
    - "incremental maintenance for leapfrog triejoin"
    - "skew strikes back new developments in the theory of join algorithms"
    - "communication steps for parallel query processing"
    - "tri tri again finding triangles and small subgraphs in a distributed setting"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-7 (induced by source paper "levelheaded making worst case optimal joins work in the common case"):
  with members:
    - "old techniques for new join algorithms a case study in rdf processing"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-8 (induced by source paper "implementation of tetris as a model counter"):
  with members:
    - "computing join queries with functional dependencies"
    - "general and fractional hypertree decompositions hard and easy cases"

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)