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 algorithms for parallel query processing"
  Abstract: In this paper, we study the communication complexity for the problem of computing a conjunctive query on a large database in a parallel setting with $p$ servers. In contrast to previous work, where upper and lower bounds on the communication were specified for particular structures of data (either data without skew, or data with specific types of skew), in this work we focus on worst-case analysis of the communication cost. The goal is to find worst-case optimal parallel algorithms, similar to the work of [18] for sequential algorithms. #R##N#We first show that for a single round we can obtain an optimal worst-case algorithm. The optimal load for a conjunctive query $q$ when all relations have size equal to $M$ is $O(M/p^{1/\psi^*})$, where $\psi^*$ is a new query-related quantity called the edge quasi-packing number, which is different from both the edge packing number and edge cover number of the query hypergraph. For multiple rounds, we present algorithms that are optimal for several classes of queries. Finally, we show a surprising connection to the external memory model, which allows us to translate parallel algorithms to external memory algorithms. This technique allows us to recover (within a polylogarithmic factor) several recent results on the I/O complexity for computing join queries, and also obtain optimal algorithms for other classes of queries.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "topology dependent bounds for faqs"):
  with members:
    - "query to communication lifting for bpp"
    - "juggling functions inside a database"
    - "tight network topology dependent bounds on rounds of communication"
    - "the range of topological effects on communication"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "faq questions asked frequently"
    - "skew in parallel query processing"
    - "aggregation and ordering in factorised databases"

Connected through hyperedge HE-3 (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"
    - "aggregations over generalized hypertree decompositions"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"
    - "leapfrog triejoin a worst case optimal join algorithm"

Connected through hyperedge HE-4 (induced by source paper "worst case optimal join algorithms techniques results and open problems"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"

Connected through hyperedge HE-5 (induced by source paper "distributed statistical estimation of matrix products with applications"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "on the complexity of inner product similarity join"
    - "communication efficient distributed kernel principal component analysis"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "skew in parallel query processing"
    - "improved distributed principal component analysis"

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"
    - "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"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-7 (induced by source paper "tight bounds for distributed graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "near optimal clustering in the k machine model"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "the input output complexity of triangle enumeration"
    - "why do simple algorithms for triangle enumeration work in the real world"

Connected through hyperedge HE-8 (induced by source paper "sharesskew an algorithm to handle skew for joins in mapreduce"):
  with members:
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "α k minimal sorting and skew join in mpi and mapreduce"
    - "skew in parallel query processing"
    - "gym a multiround join algorithm in mapreduce"
    - "the family of mapreduce and large scale data processing systems"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"
    - "communication steps for parallel query processing"

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)