human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "map estimation linear programming and belief propagation with convex free energies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convergence rates of smooth message passing with rounding in entropy regularized map inference")
  Member "tightening lp relaxations for map using message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph structured prediction energy networks")
  Member "map estimation message passing and perfect graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hinge loss markov random fields and probabilistic soft logic")
  Member "message passing for maximum weight independent set":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "throughput optimal decentralized scheduling with single bit state feedback for a class of queueing systems")
  Member "the expressive power of binary submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "binarisation for valued constraint satisfaction problems")

Question: Please tell me which class the source paper 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)