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: "computing join queries with functional dependencies"
  Abstract: Recently, Gottlob, Lee, Valiant, and Valiant (GLVV) presented an output size bound for join queries with functional dependencies (FD), based on a linear program on polymatroids. GLVV bound strictly generalizes the bound of Atserias, Grohe and Marx (AGM) for queries with no FD, in which case there are known algorithms running within AGM bound and thus are worst-case optimal. #R##N#A main result of this paper is an algorithm for computing join queries with FDs, running within GLVV bound up to a poly-log factor. In particular, our algorithm is worst-case optimal for any query where the GLVV bound is tight. As an unexpected by-product, our algorithm manages to solve a harder problem, where (some) input relations may have prescribed maximum degree bounds, of which both functional dependencies and cardinality bounds are special cases. #R##N#We extend Gottlob et al. framework by replacing all variable subsets with the lattice of closed sets (under the given FDs). This gives us new insights into the structure of the worst-case bound and worst-case instances. While it is still open whether GLVV bound is tight in general, we show that it is tight on distributive lattices and some other simple lattices. Distributive lattices capture a strict superset of queries with no FD and with simple FDs. We also present two simpler algorithms which are also worst-case optimal on distributive lattices within a single-$\log$ factor, but they do not match GLVV bound on a general lattice. Our algorithms are designed based on a novel principle: we turn a proof of a polymatroid-based output size bound into an algorithm.

Connected through hyperedge HE-1 (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"
    - "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"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-2 (induced by source paper "bag query containment and information theory"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "group characterizable entropy functions"

Connected through hyperedge HE-3 (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-4 (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"
    - "flexible caching in trie joins"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-5 (induced by source paper "answering ucqs under updates and in the presence of integrity constraints"):
  with members:
    - "dynamic conjunctive queries"
    - "answering conjunctive queries under updates"
    - "incremental view maintenance for collection programming"
    - "a trichotomy in the complexity of counting answers to conjunctive queries"
    - "structural tractability of counting of solutions to conjunctive queries"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"

Connected through hyperedge HE-6 (induced by source paper "implementation of tetris as a model counter"):
  with members:
    - "general and fractional hypertree decompositions hard and easy cases"
    - "leapfrog triejoin a worst case optimal join algorithm"

Connected through hyperedge HE-7 (induced by source paper "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "entropy bounds for conjunctive queries with functional dependencies"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "aggregation and ordering in factorised databases"
    - "skew strikes back new developments in the theory of join algorithms"
    - "piql success tolerant query processing in the cloud"

Connected through hyperedge HE-8 (induced by source paper "faq questions asked frequently"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "join processing for graph patterns an old dog with new tricks"
    - "emptyheaded boolean algebra based graph processing"
    - "joins via geometric resolutions worst case and beyond"
    - "a trichotomy in the complexity of counting answers to conjunctive 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)