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 "reconstruction non reconstruction thresholds for colourings of general galton watson trees"):
  Abstract: The broadcasting models on trees arise in many contexts such as discrete mathematics, biology statistical physics and cs. In this work, we consider the colouring model. A basic question here is whether the root's assignment affects the distribution of the colourings at the vertices at distance h from the root. This is the so-called "reconstruction problem". For a d-ary tree it is well known that d/ln (d) is the reconstruction threshold. That is, for k=(1+eps)d/ln(d) we have non-reconstruction while for k=(1-eps)d/ln(d) we have. #R##N#Here, we consider the largely unstudied case where the underlying tree is chosen according to a predefined distribution. In particular, our focus is on the well-known Galton-Watson trees. This model arises naturally in many contexts, e.g. the theory of spin-glasses and its applications on random Constraint Satisfaction Problems (rCSP). The aforementioned study focuses on Galton-Watson trees with offspring distribution B(n,d/n), i.e. the binomial with parameters n and d/n, where d is fixed. Here we consider a broader version of the problem, as we assume general offspring distribution, which includes B(n,d/n) as a special case. #R##N#Our approach relates the corresponding bounds for (non)reconstruction to certain concentration properties of the offspring distribution. This allows to derive reconstruction thresholds for a very wide family of offspring distributions, which includes B(n,d/n). A very interesting corollary is that for distributions with expected offspring d, we get reconstruction threshold d/ln(d) under weaker concentration conditions than what we have in B(n,d/n). #R##N#Furthermore, our reconstruction threshold for the random colorings of Galton-Watson with offspring B(n,d/n), implies the reconstruction threshold for the random colourings of G(n,d/n).

Includes members:
  Member "sampling colorings almost uniformly in sparse random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a simple algorithm for sampling colourings of g n d n up to gibbs uniqueness threshold")
  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 "reconstruction and clustering in random constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "broadcasting on random directed acyclic 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.DM(Discrete Mathematics)