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 "sampling colorings almost uniformly in sparse random graphs"):
  Abstract: The problem of sampling proper $q$-colorings from uniform distribution has been extensively studied. Most of existing samplers require $q\ge \alpha \Delta+\beta$ for some constants $\alpha$ and $\beta$, where $\Delta$ is the maximum degree of the graph. The problem becomes more challenging when the underlying graph has unbounded degree since even the decision of $q$-colorability becomes nontrivial in this situation. The Erd\H{o}s-R\'{e}nyi random graph $\mathcal{G}(n,d/n)$ is a typical class of such graphs and has received a lot of recent attention. In this case, the performance of a sampler is usually measured by the relation between $q$ and the average degree $d$. We are interested in the fully polynomial-time almost uniform sampler (FPAUS) and the state-of-the-art with such sampler for proper $q$-coloring on $\mathcal{G}(n,d/n)$ requires that $q\ge 5.5d$. #R##N#In this paper, we design an FPAUS for proper $q$-colorings on $\mathcal{G}(n,d/n)$ by requiring that $q\ge 3d+O(1)$, which improves the best bound for the problem so far. Our sampler is based on the spatial mixing property of $q$-coloring on random graphs. The core of the sampler is a deterministic algorithm to estimate the marginal probability on blocks, which is computed by a novel block version of recursion for $q$-coloring on unbounded degree graphs.

Includes members:
  Member "spatial mixing of coloring random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial mixing and approximate counting for potts model on graphs with bounded average degree")
  Member "mcmc sampling colourings and independent sets of g n d n near the uniqueness threshold":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling random colorings of sparse random graphs")
  Member "spatial mixing and approximation algorithms for graphs with bounded connective constant":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")
  Member "a simple algorithm for random colouring g n d n using 2 epsilon d colours":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatial mixing and approximate counting for potts model on graphs with bounded average degree")
  Member "random sampling of colourings of sparse random graphs with a constant number of colours":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatial mixing and approximate counting for potts model on graphs with bounded average degree")

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)