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: "juggling functions inside a database"
  Abstract: We define and study the Functional Aggregate Query (FAQ) problem, which captures common computational tasks across a very wide range of domains including relational databases, logic, matrix and tensor computation, probabilistic graphical models, constraint satisfaction, and signal processing. Simply put, an FAQ is a declarative way of defining a new function from a database of input functions. #R##N#We present "InsideOut", a dynamic programming algorithm, to evaluate an FAQ. The algorithm rewrites the input query into a set of easier-to-compute FAQ sub-queries. Each sub-query is then evaluated using a worst-case optimal relational join algorithm. The topic of designing algorithms to optimally evaluate the classic multiway join problem has seen exciting developments in the past few years. Our framework tightly connects these new ideas in database theory with a vast number of application areas in a coherent manner, showing potentially that a good database engine can be a general-purpose constraint solver, relational data store, graphical model inference engine, and matrix/tensor computation processor all at once. #R##N#The InsideOut algorithm is very simple, as shall be described in this paper. Yet, in spite of solving an extremely general problem, its runtime either is as good as or improves upon the best known algorithm for the applications that FAQ specializes to. These corollaries include computational tasks in graphical model inference, matrix/tensor operations, relational joins, and logic. Better yet, InsideOut can be used within any database engine, because it is basically a principled way of rewriting queries. Indeed, it is already part of the LogicBlox database engine, helping efficiently answer traditional database queries, graphical model inference queries, and train a large class of machine learning models inside the database itself.

Connected through hyperedge HE-1 (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-2 (induced by source paper "topology dependent bounds for faqs"):
  with members:
    - "query to communication lifting for bpp"
    - "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"
    - "faq questions asked frequently"
    - "skew in parallel query processing"
    - "aggregation and ordering in factorised databases"

Connected through hyperedge HE-3 (induced by source paper "on matrices and k relations"):
  with members:
    - "on the expressive power of linear algebra on graphs"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "on the expressive power of query languages for matrices"
    - "polystore mathematics of relational algebra"
    - "faq questions asked frequently"

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"
    - "worst case optimal algorithms for parallel query processing"
    - "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 "on functional aggregate queries with additive inequalities"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "worst case optimal join algorithms techniques results and open problems"
    - "learning models over relational data using sparse tensors and functional dependencies"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "boolean tensor decomposition for conjunctive queries with negation"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "faq questions asked frequently"

Connected through hyperedge HE-6 (induced by source paper "repair based degrees of database inconsistency computation and complexity"):
  with members:
    - "computing optimal repairs for functional dependencies"
    - "consistency and trust in peer data exchange systems"
    - "achieving data privacy through secrecy views and null based virtual updates"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "prioritized repairing and consistent query answering in relational databases"
    - "the dlv system for knowledge representation and reasoning"
    - "complexity of consistent query answering in databases under cardinality based and incremental repair semantics"
    - "minimal change integrity maintenance using tuple deletions"

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)