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: "solving constraint satisfaction problems through belief propagation guided decimation"
  Abstract: Message passing algorithms have proved surprisingly successful in solving hard constraint satisfaction problems on sparse random graphs. In such applications, variables are fixed sequentially to satisfy the constraints. Message passing is run after each step. Its outcome provides an heuristic to make choices at next step. This approach has been referred to as `decimation,' with reference to analogous procedures in statistical physics. #R##N#The behavior of decimation procedures is poorly understood. Here we consider a simple randomized decimation algorithm based on belief propagation (BP), and analyze its behavior on random k-satisfiability formulae. In particular, we propose a tree model for its analysis and we conjecture that it provides asymptotically exact predictions in the limit of large instances. This conjecture is confirmed by numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep sets"
    - "streamlining variational inference for constraint satisfaction problems"
    - "inference in probabilistic graphical models by graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-3 (induced by source paper "perturbed message passing for constraint satisfaction problems"):
  with members:
    - "revisiting algebra and complexity of inference in graphical models"
    - "survey propagation revisited"
    - "belief propagation for continuous state spaces stochastic message passing with quantitative guarantees"
    - "xml representation of constraint networks format xcsp 2 1"
    - "a new look at survey propagation and its generalizations"
    - "survey propagation an algorithm for satisfiability"

Connected through hyperedge HE-4 (induced by source paper "aspects of statistical physics in computational complexity"):
  with members:
    - "counting good truth assignments of random k sat formulae"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-5 (induced by source paper "decimation enhanced finite alphabet iterative decoders for ldpc codes on the bsc"):
  with members:
    - "multilevel decoders surpassing belief propagation on the binary symmetric channel"
    - "guessing facets polytope structure and improved lp decoder"
    - "reducing the error floor"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"
    - "pseudocodewords of tanner graphs"

Connected through hyperedge HE-6 (induced by source paper "belief propagation for min cost network flow convergence and correctness"):
  with members:
    - "message passing for maximum weight independent set"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "convergence of the min sum message passing algorithm for quadratic optimization"

Connected through hyperedge HE-7 (induced by source paper "polar codes are optimal for lossy source coding"):
  with members:
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "performance of polar codes for channel and source coding"
    - "polar codes characterization of exponent bounds and constructions"
    - "polarization for arbitrary discrete memoryless channels"
    - "on the rate of channel polarization"
    - "low density graph codes that are optimal for source channel coding and binning"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"

Connected through hyperedge HE-8 (induced by source paper "random subcubes as a toy model for constraint satisfaction problems"):
  with members:
    - "on the solution space geometry of random constraint satisfaction problems"
    - "counting good truth assignments of random k sat formulae"
    - "some remarks on the survey decimation algorithm for k satisfiability"

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.AI(Artificial Intelligence)