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: "on map inference by mwss on perfect graphs"
  Abstract: Finding the most likely (MAP) configuration of a Markov random field (MRF) is NP-hard in general. A promising, recent technique is to reduce the problem to finding a maximum weight stable set (MWSS) on a derived weighted graph, which if perfect, allows inference in polynomial time. We derive new results for this approach, including a general decomposition theorem for MRFs of any order and number of labels, extensions of results for binary pairwise models with submodular cost functions to higher order, and an exact characterization of which binary pairwise MRFs can be efficiently solved with this method. This defines the power of the approach on this class of models, improves our toolbox and expands the range of tractable models.

Connected through hyperedge HE-1 (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"
    - "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"
    - "optimization with parity constraints from binary codes to discrete integration"

Connected through hyperedge HE-2 (induced by source paper "revisiting algebra and complexity of inference in graphical models"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "hybrid processing of beliefs and constraints"
    - "survey propagation revisited"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "an algebraic graphical model for decision with uncertainties feasibilities and utilities"
    - "complexity results and approximation strategies for map explanations"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-3 (induced by source paper "algebra of inference in graphical models revisited"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "hybrid processing of beliefs and constraints"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "complexity results and approximation strategies for map explanations"
    - "a new look at survey propagation and its generalizations"

Connected through hyperedge HE-4 (induced by source paper "approximating the bethe partition function"):
  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"
    - "bethe bounds and approximating the global optimum"
    - "what cannot be learned with bethe approximations"
    - "on the partition function and random maximum a posteriori perturbations"
    - "convexifying the bethe free energy"
    - "complexity of inference in graphical models"

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)