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 the generation of alternative explanations with implications for belief revision"
  Abstract: In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of message-passing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In this paper, we present a general approach based on linear constraint systems that naturally generates alternative explanations in an orderly and highly efficient manner. This approach is then applied to cost-based abduction problems as well as belief revision in Bayesian net works.

Connected through hyperedge HE-1 (induced by source paper "tighter linear program relaxations for high order graphical models"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "context specific independence in bayesian networks"
    - "fast exact inference for recursive cardinality models"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-2 (induced by source paper "an efficient approach for finding the mpe in belief networks"):
  with members:
    - "a new algorithm for finding map assignments to belief networks"
    - "qualitative propagation and scenario based explanation of probabilistic reasoning"
    - "d separation from theorems to algorithms"

Connected through hyperedge HE-3 (induced by source paper "tightening lp relaxations for map using message passing"):
  with members:
    - "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"
    - "structured region graphs morphing ep into gbp"
    - "on the optimality of tree reweighted max product message passing"

Connected through hyperedge HE-4 (induced by source paper "map estimation linear programming and belief propagation with convex free energies"):
  with members:
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "systematic vs non systematic algorithms for solving the mpe task"
    - "on the optimality of tree reweighted max product message passing"
    - "a new class of upper bounds on the log partition function"

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)