human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on functional aggregate queries with additive inequalities"):
  Abstract: Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables. We refer to these queries as FAQ-AI for short. #R##N#To answer FAQ-AI in the Boolean semiring, we define relaxed tree decompositions and relaxed submodular and fractional hypertree width parameters. We show that an extension of the InsideOut algorithm using Chazelle's geometric data structure for solving the semigroup range search problem can answer Boolean FAQ-AI in time given by these new width parameters. This new algorithm achieves lower complexity than known solutions for FAQ-AI. It also recovers some known results in database query answering. #R##N#Our second contribution is a relaxation of the set of polymatroids that gives rise to the counting version of the submodular width, denoted by #subw. This new width is sandwiched between the submodular and the fractional hypertree widths. Any FAQ and FAQ-AI over one semiring can be answered in time proportional to #subw and respectively to the relaxed version of #subw. #R##N#We present three applications of our FAQ-AI framework to relational machine learning: k-means clustering, training linear support vector machines, and training models using non-polynomial loss. These optimization problems can be solved over a database asymptotically faster than computing the join of the database relations.

Includes members:
  Member "ac dc in database learning thunderstruck":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "worst case optimal join algorithms techniques results and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rk means fast clustering for relational data")
  Member "learning models over relational data using sparse tensors and functional dependencies":
    also connected through other hyperedges:
      (none)
  Member "constraint solving via fractional edge covers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "in database learning with sparse tensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spores sum product optimization via relational equality saturation for large scale linear algebra")
  Member "juggling functions inside a database":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rk means fast clustering for relational data")
  Member "boolean tensor decomposition for conjunctive queries with negation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning models over relational data a brief tutorial")
  Member "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constant delay enumeration with fpt preprocessing for conjunctive queries of bounded submodular width")

Question: Please tell me which class the source paper 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)