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: "join processing for graph patterns an old dog with new tricks"
  Abstract: Join optimization has been dominated by Selinger-style, pairwise optimizers for decades. But, Selinger-style algorithms are asymptotically suboptimal for applications in graphic analytics. This suboptimality is one of the reasons that many have advocated supplementing relational engines with specialized graph processing engines. Recently, new join algorithms have been discovered that achieve optimal worst-case run times for any join or even so-called beyond worst-case (or instance optimal) run time guarantees for specialized classes of joins. These new algorithms match or improve on those used in specialized graph-processing systems. This paper asks can these new join algorithms allow relational engines to close the performance gap with graph engines? #R##N#We examine this question for graph-pattern queries or join queries. We find that classical relational databases like Postgres and MonetDB or newer graph databases/stores like Virtuoso and Neo4j may be orders of magnitude slower than these new approaches compared to a fully featured RDBMS, LogicBlox, using these new ideas. Our results demonstrate that an RDBMS with such new algorithms can perform as well as specialized engines like GraphLab -- while retaining a high-level interface. We hope this adds to the ongoing debate of the role of graph accelerators, new graph systems, and relational systems in modern workloads.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "faq questions asked frequently"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"

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

Connected through hyperedge HE-5 (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"
    - "worst case optimal algorithms for parallel query processing"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"

Connected through hyperedge HE-6 (induced by source paper "in database learning with sparse tensors"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "research directions for principles of data management dagstuhl perspectives workshop 16151"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "on the expressive power of query languages for matrices"
    - "towards linear algebra over normalized data"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "juggling functions inside a database"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"
    - "flexible caching in trie joins"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-8 (induced by source paper "flexible caching in trie joins"):
  with members:
    - "aggregations over generalized hypertree decompositions"
    - "emptyheaded boolean algebra based graph processing"
    - "faq questions asked frequently"
    - "aggregation and ordering in factorised databases"
    - "skew strikes back new developments in the theory of join algorithms"
    - "hypertree decompositions and tractable queries"

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)