human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "mcmc sampling colourings and independent sets of g n d n near the uniqueness threshold"
  Abstract: Sampling from Gibbs distribution is a central problem in computer science as well as in statistical physics. In this work we focus on the k-colouring model} and the hard-core model with fugacity \lambda when the underlying graph is an instance of Erdos-Renyi random graph G(n,p), where p=d/n and d is fixed. #R##N#We use the Markov Chain Monte Carlo method for sampling from the aforementioned distributions. In particular, we consider Glauber (block) dynamics. We show a dramatic improvement on the bounds for rapid mixing in terms of the number of colours and the fugacity for the corresponding models. For both models the bounds we get are only within small constant factors from the conjectured ones by the statistical physicists. #R##N#We use Path Coupling to show rapid mixing. For k and \lambda in the range of our interest the technical challenge is to cope with the high degree vertices, i.e. vertices of degree much larger than the expected degree d. The usual approach to this problem is to consider block updates rather than single vertex updates for the Markov chain. Taking appropriately defined blocks the effect of high degree vertices somehow diminishes. However, devising such a construction of blocks is a highly non trivial task. #R##N#We develop for a first time a weighting schema for the paths of the underlying graph. Vertices which belong to "light" paths, only, can be placed at the boundaries of the blocks. Then the tree-like local structure of G(n,d/n) allows the construction of simple structured blocks.

Connected through hyperedge HE-1 (induced by source paper "sampling random colorings of sparse random graphs"):
  with members:
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (induced by source paper "a simple algorithm for sampling colourings of g n d n up to gibbs uniqueness threshold"):
  with members:
    - "sampling colorings almost uniformly in sparse random graphs"

Connected through hyperedge HE-3 (induced by source paper "spatial mixing and approximate counting for potts model on graphs with bounded average degree"):
  with members:
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "spatial mixing of coloring random graphs"
    - "chasing the k colorability threshold"
    - "spatial mixing and approximation algorithms for graphs with bounded connective constant"
    - "inapproximability for antiferromagnetic spin systems in the tree non uniqueness region"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"
    - "a simple algorithm for random colouring g n d n using 2 epsilon d colours"

Connected through hyperedge HE-4 (induced by source paper "sampling colorings almost uniformly in sparse random graphs"):
  with members:
    - "spatial mixing of coloring random graphs"
    - "spatial mixing and approximation algorithms for graphs with bounded connective constant"
    - "a simple algorithm for random colouring g n d n using 2 epsilon d colours"
    - "random sampling of colourings of sparse random graphs with a constant number of colours"

Connected through hyperedge HE-5 (induced by source paper "spatial mixing and the connective constant optimal bounds"):
  with members:
    - "spatial mixing and approximation algorithms for graphs with bounded connective constant"
    - "improved bounds on the phase transition for the hard core model in 2 dimensions"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"
    - "approximate counting via correlation decay in spin systems"
    - "improved inapproximability results for counting independent sets in the hard core model"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-6 (induced by source paper "spatial mixing of coloring random graphs"):
  with members:
    - "chasing the k colorability threshold"
    - "spatial mixing and approximation algorithms for graphs with bounded connective constant"
    - "a simple algorithm for random colouring g n d n using 2 epsilon d colours"

Connected through hyperedge HE-7 (induced by source paper "reconstruction non reconstruction thresholds for colourings of general galton watson trees"):
  with members:
    - "sampling colorings almost uniformly in sparse random graphs"
    - "reconstruction and clustering in random constraint satisfaction problems"

Connected through hyperedge HE-8 (induced by source paper "spatial mixing and approximation algorithms for graphs with bounded connective constant"):
  with members:
    - "improved bounds on the phase transition for the hard core model in 2 dimensions"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"
    - "approximate counting via correlation decay in spin systems"
    - "computational transition at the uniqueness threshold"

Question: Please tell me which class the center node 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.DM(Discrete Mathematics)