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: "map estimation message passing and perfect graphs"
  Abstract: Efficiently finding the maximum a posteriori (MAP) configuration of a graphical model is an important problem which is often implemented using message passing algorithms. The optimality of such algorithms is only well established for singly-connected graphs and other limited settings. This article extends the set of graphs where MAP estimation is in P and where message passing recovers the exact solution to so-called perfect graphs. This result leverages recent progress in defining perfect graphs (the strong perfect graph theorem), linear programming relaxations of MAP estimation and recent convergent message passing schemes. The article converts graphical models into nand Markov random fields which are straightforward to relax into linear programs. Therein, integrality can be established in general by testing for graph perfection. This perfection test is performed efficiently using a polynomial time algorithm. Alternatively, known decomposition tools from perfect graph theory may be used to prove perfection for certain families of graphs. Thus, a general graph framework is provided for determining when MAP estimation in any graphical model is in P, has an integral linear programming relaxation and is exactly recoverable by message passing.

Connected through hyperedge HE-1 (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "exploiting social network structure for person to person sentiment analysis"
    - "inferring user preferences by probabilistic logical reasoning over social networks"
    - "tighter linear program relaxations for high order graphical models"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "a hypergraph partitioned vertex programming approach for large scale consensus optimization"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-2 (induced by source paper "on map inference by mwss on perfect graphs"):
  with members:
    - "map estimation linear programming and belief propagation with convex free energies"
    - "tightening lp relaxations for map using message passing"
    - "message passing for maximum weight independent set"
    - "the expressive power of binary submodular functions"

Connected through hyperedge HE-3 (induced by source paper "exploiting structure in cooperative bayesian games"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-4 (induced by source paper "exploiting agent and type independence in collaborative graphical bayesian games"):
  with members:
    - "graphical models for game theory"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "policy iteration for factored mdps"
    - "exploiting structure in cooperative bayesian games"
    - "rollout sampling policy iteration for decentralized pomdps"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "factored value iteration converges"

Connected through hyperedge HE-5 (induced by source paper "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions"):
  with members:
    - "map estimation linear programming and belief propagation with convex free energies"
    - "a new class of upper bounds on the log partition function"
    - "message passing for maximum weight independent set"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "equivalence of lp relaxation and max product for weighted matching in general graphs"
    - "convergence of the min sum message passing algorithm for quadratic optimization"
    - "on the solution space geometry of random constraint satisfaction problems"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-6 (induced by source paper "hybrid tractability of valued constraint problems"):
  with members:
    - "the complexity of conservative valued csps"
    - "generalising tractable vcsps defined by symmetric tournament pair multimorphisms"
    - "using pivot consistency to decompose and solve functional csps"

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)