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: "structured region graphs morphing ep into gbp"
  Abstract: GBP and EP are two successful algorithms for approximate probabilistic inference, which are based on different approximation strategies. An open problem in both algorithms has been how to choose an appropriate approximation structure. We introduce 'structured region graphs', a formalism which marries these two strategies, reveals a deep connection between them, and suggests how to choose good approximation structures. In this formalism, each region has an internal structure which defines an exponential family, whose sufficient statistics must be matched by the parent region. Reduction operators on these structures allow conversion between EP and GBP free energies. Thus it is revealed that all EP approximations on discrete variables are special cases of GBP, and conversely that some wellknown GBP approximations, such as overlapping squares, are special cases of EP. Furthermore, region graphs derived from EP have a number of good structural properties, including maxent-normality and overall counting number of one. The result is a convenient framework for producing high-quality approximations with a user-adjustable level of complexity

Connected through hyperedge HE-1 (induced by source paper "region based approximation for high dimensional bayesian network models"):
  with members:
    - "performing bayesian risk aggregation using discrete approximation algorithms with graph factorization"
    - "nonuniform dynamic discretization in hybrid networks"
    - "loopy belief propagation for approximate inference an empirical study"
    - "generalized belief propagation on tree robust structured region graphs"
    - "on the choice of regions for generalized belief propagation"
    - "join graph propagation algorithms"

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 "performing bayesian risk aggregation using discrete approximation algorithms with graph factorization"):
  with members:
    - "learning bayesian networks with local structure"
    - "expectation propagation for approximate bayesian inference"
    - "generalized belief propagation on tree robust structured region graphs"
    - "on the choice of regions for generalized belief propagation"
    - "convergent message passing algorithms a unifying view"
    - "join graph propagation algorithms"
    - "gaussian belief propagation based multiuser detection"
    - "truncating the loop series expansion for belief propagation"

Connected through hyperedge HE-4 (induced by source paper "tree structure expectation propagation for ldpc decoding over the bec"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "analytical solution of covariance evolution for irregular ldpc codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "tree structure expectation propagation for decoding ldpc codes over binary erasure channels"
    - "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "on the stopping distance and the stopping redundancy of codes"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-5 (induced by source paper "structured message passing"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation propagation for approximate bayesian inference"
    - "context specific independence in bayesian networks"
    - "approximation by quantization"
    - "and or importance sampling"
    - "probabilistic theorem proving"
    - "join graph propagation algorithms"

Connected through hyperedge HE-6 (induced by source paper "generalized belief propagation on tree robust structured region graphs"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "sufficient conditions for convergence of loopy belief propagation"
    - "on the choice of regions for generalized belief propagation"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "iterative join graph propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "convergent message passing algorithms a unifying view"

Connected through hyperedge HE-7 (induced by source paper "tightening lp relaxations for map using message passing"):
  with members:
    - "on the generation of alternative explanations with implications for belief revision"
    - "on the choice of regions for generalized belief propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "on the optimality of tree reweighted max product message passing"

Connected through hyperedge HE-8 (induced by source paper "approximating the partition function by deleting and then correcting for model edges"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "variational approximations between mean field theory and the junction tree algorithm"
    - "iterative join graph propagation"
    - "a variational approach for approximating bayesian networks by edge deletion"

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)