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 "local access to huge random objects through partial sampling"):
  Abstract: Consider an algorithm performing a computation on a huge random object. Is it necessary to generate the entire object up front, or is it possible to provide query access to the object and sample it incrementally "on-the-fly"? Such an implementation should emulate the object by answering queries in a manner consistent with a random instance sampled from the true distribution.  Our first set of results focus on undirected graphs with independent edge probabilities, under certain assumptions. Then, we use this to obtain the first efficient implementations for the Erdos-Renyi model and the Stochastic Block model. As in previous local-access implementations for random graphs, we support Vertex-Pair and Next-Neighbor queries. We also introduce a new Random-Neighbor query.  Next, we show how to implement random Catalan objects, specifically focusing on Dyck paths (always positive random walks on the integer line). Here, we support Height queries to find the position of the walk, and First-Return queries to find the time when the walk returns to a specified height. This in turn can be used to implement Next-Neighbor queries on random rooted/binary trees, and Matching-Bracket queries on random well bracketed expressions.  Finally, we define a new model that: (1) allows multiple independent simultaneous instantiations of the same implementation to be consistent with each other without communication (2) allows us to generate a richer class of random objects that do not have a succinct description. Specifically, we study uniformly random valid $q$-colorings of an input graph $G$ with max degree $\Delta$. The distribution over valid colorings is specified via a "huge" underlying graph $G$, that is far too large to be read in sub-linear time. Instead, we access $G$ through local neighborhood probes. We are able to answer queries to the color of any vertex in sub-linear time for $q > 9\Delta$.

Includes members:
  Member "a simple parallel and distributed sampling technique local glauber dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed metropolis sampler with optimal parallelism")
  Member "on the probe complexity of local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local access generators for basic random graph models")
  Member "sublinear random access generators for preferential attachment graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local access generators for basic random graph models")
  Member "parallel algorithms for generating random networks with given degree sequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a parallel algorithm for generating a random graph with a prescribed degree sequence")
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "fast local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on local computation for optimization in multi agent systems")

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)