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 "sublinear random access generators for preferential attachment graphs"):
  Abstract: We consider the problem of sampling from a distribution on graphs, specifically when the distribution is defined by an evolving graph model, and consider the time, space and randomness complexities of such samplers. #R##N#In the standard approach, the whole graph is chosen randomly according to the randomized evolving process, stored in full, and then queries on the sampled graph are answered by simply accessing the stored graph. This may require prohibitive amounts of time, space and random bits, especially when only a small number of queries are actually issued. Instead, we propose to generate the graph on-the-fly, in response to queries, and therefore to require amounts of time, space, and random bits which are a function of the actual number of queries. #R##N#We focus on two random graph models: the Barab{\'{a}}si-Albert Preferential Attachment model (BA-graphs) and the random recursive tree model. We give on-the-fly generation algorithms for both models. With probability $1-1/\mbox{poly}(n)$, each and every query is answered in $\mbox{polylog}(n)$ time, and the increase in space and the number of random bits consumed by any single query are both $\mbox{polylog}(n)$, where $n$ denotes the number of vertices in the graph. #R##N#Our results show that, although the BA random graph model is defined by a sequential process, efficient random access to the graph's nodes is possible. In addition to the conceptual contribution, efficient on-the-fly generation of random graphs can serve as a tool for the efficient simulation of sublinear algorithms over large BA-graphs, and the efficient estimation of their performance on such graphs.

Includes members:
  Member "a scalable generative graph model with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances in scalable network generation")
  Member "new techniques and tighter bounds for local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "best of two local models local centralized and local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local computation algorithms for graphs of non constant degrees")
  Member "local algorithms for sparse spanning graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "a local computation approximation scheme to maximum matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "converting online algorithms to local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "space efficient local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "a near optimal sublinear time algorithm for approximating the minimum vertex cover size":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing")

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)