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 "a new look at reweighted message passing"):
  Abstract: We propose a new family of message passing techniques for MAP estimation in graphical models which we call   Sequential Reweighted Message Passing  (SRMP). Special cases include well-known techniques such as   Min-Sum Diffusion  (MSD) and a faster  Sequential Tree-Reweighted Message Passing  (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. The new family of algorithms can be viewed as a generalization of TRW-S from pairwise to higher-order graphical models. We test SRMP on several real-world problems with promising results.

Includes members:
  Member "generalized sequential tree reweighted message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient graph cut optimization for full crfs with quantized edges")
  Member "efficient mrf energy minimization via adaptive diminishing smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "map inference via l2 sphere linear program reformulation")
  Member "the power of linear programming for valued csps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "piecewise linear valued csps solvable by linear programming relaxation")
  Member "convergent message passing algorithms a unifying view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph structured prediction energy networks")
  Member "tightening lp relaxations for map using message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph structured prediction energy networks")
  Member "efficiently searching for frustrated cycles in map inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph structured prediction energy networks")
  Member "the power of linear programming for valued csps a constructive characterization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the complexity of finite valued csps")
  Member "norm product belief propagation primal dual message passing for approximate inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable explanation of inferences on large graphs")

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)