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: "faq questions asked frequently"
  Abstract: We define and study the Functional Aggregate Query (FAQ) problem, which encompasses many frequently asked questions in constraint satisfaction, databases, matrix operations, probabilistic graphical models and logic. This is our main conceptual contribution. #R##N#We then present a simple algorithm called "InsideOut" to solve this general problem. InsideOut is a variation of the traditional dynamic programming approach for constraint programming based on variable elimination. Our variation adds a couple of simple twists to basic variable elimination in order to deal with the generality of FAQ, to take full advantage of Grohe and Marx's fractional edge cover framework, and of the analysis of recent worst-case optimal relational join algorithms. #R##N#As is the case with constraint programming and graphical model inference, to make InsideOut run efficiently we need to solve an optimization problem to compute an appropriate 'variable ordering'. The main technical contribution of this work is a precise characterization of when a variable ordering is 'semantically equivalent' to the variable ordering given by the input FAQ expression. Then, we design an approximation algorithm to find an equivalent variable ordering that has the best 'fractional FAQ-width'. Our results imply a host of known and a few new results in graphical model inference, matrix operations, relational joins, and logic. #R##N#We also briefly explain how recent algorithms on beyond worst-case analysis for joins and those for solving SAT and #SAT can be viewed as variable elimination to solve FAQ over compactly represented input functions.

Connected through hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "myhill nerode methods for hypergraphs"

Connected through hyperedge HE-2 (induced by source paper "multi layer optimizations for end to end data analytics"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "ac dc in database learning thunderstruck"
    - "efficient differentiable programming in a functional array processing language"
    - "stream fusion to completeness"
    - "building efficient query engines in a high level language"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "hpat high performance analytics with scripting ease of use"

Connected through hyperedge HE-3 (induced by source paper "spores sum product optimization via relational equality saturation for large scale linear algebra"):
  with members:
    - "using e graphs for cad parameter inference"
    - "on optimizing operator fusion plans for large scale machine learning in systemml"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "in database learning with sparse tensors"
    - "solving the join ordering problem via mixed integer linear programming"
    - "equality saturation a new approach to optimization"

Connected through hyperedge HE-4 (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-5 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "on functional aggregate queries with additive inequalities"
    - "coresets for k means and k median clustering and their applications"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "clustering high dimensional dynamic data streams"
    - "in database learning with sparse tensors"

Connected through hyperedge HE-6 (induced by source paper "a layered aggregate engine for analytics workloads"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "in database learning with sparse tensors"
    - "building efficient query engines in a high level language"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"
    - "the ctu prague relational learning repository"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-7 (induced by source paper "topology dependent bounds for faqs"):
  with members:
    - "query to communication lifting for bpp"
    - "juggling functions inside a database"
    - "worst case optimal algorithms for parallel query processing"
    - "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"
    - "skew in parallel query processing"
    - "aggregation and ordering in factorised databases"

Connected through hyperedge HE-8 (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"
    - "joins via geometric resolutions worst case and beyond"
    - "faster compressed quadtrees"
    - "beyond worst case analysis for joins with minesweeper"

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)