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: "a delayed column generation strategy for exact k bounded map inference in markov logic networks"
  Abstract: The paper introduces k-bounded MAP inference, a parameterization of MAP inference in Markov logic networks. k-Bounded MAP states are MAP states with at most k active ground atoms of hidden (non-evidence) predicates. We present a novel delayed column generation algorithm and provide empirical evidence that the algorithm efficiently computes k-bounded MAP states for meaningful real-world graph matching problems. The underlying idea is that, instead of solving one large optimization problem, it is often more efficient to tackle several small ones.

Connected through hyperedge HE-1 (induced by source paper "rockit exploiting parallelism and symmetry for map inference in statistical relational models"):
  with members:
    - "automorphism groups of graphical models and lifted variational inference"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "exploiting uniform assignments in first order mpe"
    - "probabilistic theorem proving"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-2 (induced by source paper "markov chains on orbits of permutation groups"):
  with members:
    - "lower complexity bounds for lifted inference"
    - "practical graph isomorphism ii"
    - "general purpose mcmc inference over relational structures"
    - "bisimulation based approximate lifted inference"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "symmetry breaking answer set solving"

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)