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: "skew strikes back new developments in the theory of join algorithms"
  Abstract: Evaluating the relational join is one of the central algorithmic and most well-studied problems in database systems. A staggering number of variants have been considered including Block-Nested loop join, Hash-Join, Grace, Sort-merge for discussions of more modern issues). Commercial database engines use finely tuned join heuristics that take into account a wide variety of factors including the selectivity of various predicates, memory, IO, etc. In spite of this study of join queries, the textbook description of join processing is suboptimal. This survey describes recent results on join algorithms that have provable worst-case optimality runtime guarantees. We survey recent work and provide a simpler and unified description of these algorithms that we hope is useful for theory-minded readers, algorithm designers, and systems implementors.

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 "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"
    - "size bounds and query plans for relational joins"
    - "incremental view maintenance with triple lock factorization benefits"
    - "constraint solving via fractional edge covers"
    - "boolean tensor decomposition for conjunctive queries with negation"

Connected through hyperedge HE-3 (induced by source paper "optimal joins using compact data structures"):
  with members:
    - "worst case optimal join algorithms techniques results and open problems"
    - "size bounds and query plans for relational joins"
    - "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"

Connected through hyperedge HE-4 (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"
    - "leapfrog triejoin a worst case optimal join algorithm"

Connected through hyperedge HE-5 (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"
    - "size bounds and query plans for relational joins"
    - "answering conjunctive queries under updates"
    - "compressed representations of conjunctive query results"
    - "fdb a query engine for factorised relational databases"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-6 (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"
    - "flexible caching in trie joins"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "beyond worst case analysis for joins with minesweeper"

Connected through hyperedge HE-7 (induced by source paper "worst case optimal radix triejoin"):
  with members:
    - "optimal joins using compact data structures"
    - "counting triangles under updates in worst case optimal time"
    - "constraint solving via fractional edge covers"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"

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"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "worst case optimal join algorithms"

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)