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: "approximating the partition function by deleting and then correcting for model edges"
  Abstract: We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deleting model edges, and (2) rectifying the result by applying an edge-by-edge correction. The approach leads to an intuitive framework in which one can trade-off the quality of an approximation with the complexity of computing it. It also includes the Bethe free energy approximation as a degenerate case. We develop the approach theoretically in this paper and provide a number of empirical results that reveal its practical utility.

Connected through hyperedge HE-1 (induced by source paper "dual decomposition from the perspective of relax compensate and then recover"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "iterative join graph propagation"
    - "tightening lp relaxations for map using message passing"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-2 (induced by source paper "clamping improves trw and mean field approximations"):
  with members:
    - "approximating the bethe partition function"
    - "efficiently searching for frustrated cycles in map inference"
    - "a new class of upper bounds on the log partition function"
    - "the bethe partition function of log supermodular graphical models"

Connected through hyperedge HE-3 (induced by source paper "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"):
  with members:
    - "bisimulation based approximate lifted inference"
    - "iterative join graph propagation"
    - "node splitting a scheme for generating upper bounds in bayesian networks"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

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.LG(Machine Learning)