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: "empirical evaluation of approximation algorithms for probabilistic decoding"
  Abstract: It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [McEliece]. Moreover, it was observed that iterative application of the (linear time) Pearl's belief propagation algorithm designed for polytrees outperformed state of the art decoding algorithms, even though the corresponding networks may have many cycles. This paper demonstrates empirically that an approximation algorithm approx-mpe for solving the most probable explanation (MPE) problem, developed within the recently proposed mini-bucket elimination framework [Dechter96], outperforms iterative belief propagation on classes of coding networks that have bounded induced width. Our experiments suggest that approximate MPE decoders can be good competitors to the approximate belief updating decoders.

Connected through hyperedge HE-1 (induced by source paper "mini bucket heuristics for improved search"):
  with members:
    - "a new algorithm for finding map assignments to belief networks"
    - "a scheme for approximating probabilistic inference"

Connected through hyperedge HE-2 (induced by source paper "cutset sampling with likelihood weighting"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "backward simulation in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "an importance sampling algorithm based on evidence pre propagation"
    - "an empirical study of w cutset sampling for bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "node splitting a scheme for generating upper bounds in bayesian networks"):
  with members:
    - "systematic vs non systematic algorithms for solving the mpe task"
    - "iterative join graph propagation"
    - "a variational approach for approximating bayesian networks by edge deletion"
    - "on the optimality of tree reweighted max product message passing"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "cutset sampling for bayesian networks"):
  with members:
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "loopy belief propagation for approximate inference an empirical study"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "global conditioning for probabilistic inference in belief networks"
    - "an importance sampling algorithm based on evidence pre propagation"

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)