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 "efficient logspace classes for enumeration counting and uniform generation"):
  Abstract: In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like information extraction and graph databases, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting and uniform generation of solutions, and show that they have several desirable properties in this respect. #R##N#Both complexity classes are defined in terms of non-deterministic logspace transducers (NL transducers). For the first class, we consider the case of unambiguous NL transducers, and we prove constant delay enumeration, and both counting and uniform generation of solutions in polynomial time. For the second class, we consider unrestricted NL transducers, and we obtain polynomial delay enumeration, approximate counting in polynomial time, and polynomial-time randomized algorithms for uniform generation. More specifically, we show that each problem in this second class admits a fully polynomial-time randomized approximation scheme (FPRAS) and a polynomial-time Las Vegas algorithm for uniform generation. Interestingly, the key idea to prove these results is to show that the fundamental problem $\text{#NFA}$ admits an FPRAS, where $\text{#NFA}$ is the problem of counting the number of strings of length $n$ (given in unary) accepted by a non-deterministic finite automaton (NFA). While this problem is known to be $\text{#P}$-complete and, more precisely, $\text{SpanL}$-complete, it was open whether this problem admits an FPRAS. In this work, we solve this open problem, and obtain as a welcome corollary that every function in $\text{SpanL}$ admits an FPRAS.

Includes members:
  Member "connecting knowledge compilation classes and width parameters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph width measures for cnf encodings with auxiliary variables")
  Member "constant delay enumeration for nondeterministic document spanners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grammars for document spanenrs")
  Member "descriptive complexity for counting complexity classes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pumping lemmas for weighted automata")
  Member "constant delay algorithms for regular document spanners":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grammars for document spanenrs")
  Member "a circuit based approach to efficient enumeration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "connecting knowledge compilation classes and width parameters")
  Member "document spanners for extracting incomplete information expressiveness and complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grammars for document spanenrs")
  Member "joining extractions of regular expressions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grammars for document spanenrs")
  Member "foundations of modern query languages for graph databases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graphs")

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.DS(Data Structures and Algorithms)