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 "perturbed message passing for constraint satisfaction problems"):
  Abstract: We introduce an efficient message passing scheme for solving Constraint Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and directly produce a single satisfying assignment. Our first CSP solver, called Perturbed Blief Propagation, smoothly interpolates two well-known inference procedures; it starts as BP and ends as a Gibbs sampler, which produces a single sample from the set of solutions. Moreover we apply a similar perturbation scheme to SP to produce another CSP solver, Perturbed Survey Propagation. Experimental results on random and real-world CSPs show that Perturbed BP is often more successful and at the same time tens to hundreds of times more efficient than standard BP guided decimation. Perturbed BP also compares favorably with state-of-the-art SP-guided decimation, which has a computational complexity that generally scales exponentially worse than our method (wrt the cardinality of variable domains and constraints). Furthermore, our experiments with random satisfiability and coloring problems demonstrate that Perturbed SP can outperform SP-guided decimation, making it the best incomplete random CSP-solver in difficult regimes.

Includes members:
  Member "revisiting algebra and complexity of inference in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "message passing and combinatorial optimization")
  Member "survey propagation revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "message passing and combinatorial optimization")
  Member "belief propagation for continuous state spaces stochastic message passing with quantitative guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "message passing and combinatorial optimization")
  Member "xml representation of constraint networks format xcsp 2 1":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "message passing and combinatorial optimization")
  Member "solving constraint satisfaction problems through belief propagation guided decimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers")
  Member "a new look at survey propagation and its generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "streamlining variational inference for constraint satisfaction problems")
  Member "survey propagation an algorithm for satisfiability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers")

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.AI(Artificial Intelligence)