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 "analysis of the min sum algorithm for packing and covering problems via linear programming"):
  Abstract: Message-passing algorithms based on belief-propagation (BP) are successfully used in many applications including decoding error correcting codes and solving constraint satisfaction and inference problems. BP-based algorithms operate over graph representations, called factor graphs, that are used to model the input. Although in many cases BP-based algorithms exhibit impressive empirical results, not much has been proved when the factor graphs have cycles. #R##N#This work deals with packing and covering integer programs in which the constraint matrix is zero-one, the constraint vector is integral, and the variables are subject to box constraints. We study the performance of the min-sum algorithm when applied to the corresponding factor graph models of packing and covering LPs. #R##N#We compare the solutions computed by the min-sum algorithm for packing and covering problems to the optimal solutions of the corresponding linear programming (LP) relaxations. In particular, we prove that if the LP has an optimal fractional solution, then for each fractional component, the min-sum algorithm either computes multiple solutions or the solution oscillates below and above the fraction. This implies that the min-sum algorithm computes the optimal integral solution only if the LP has a unique optimal solution that is integral. #R##N#The converse is not true in general. For a special case of packing and covering problems, we prove that if the LP has a unique optimal solution that is integral and on the boundary of the box constraints, then the min-sum algorithm computes the optimal solution in pseudo-polynomial time. #R##N#Our results unify and extend recent results for the maximum weight matching problem by [Sanghavi et al.,'2011] and [Bayati et al., 2011] and for the maximum weight independent set problem [Sanghavi et al.'2009].

Includes members:
  Member "message passing algorithms reparameterizations and splittings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "message passing and combinatorial optimization")
  Member "smoothed analysis of belief propagation for minimum cost flow and matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smoothed complexity theory")
  Member "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pils exploring high order neighborhoods by pattern mining and injection")
  Member "on decoding irregular tanner codes with local optimality guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local optimality guarantees for optimal decoding based on paths")
  Member "belief propagation for min cost network flow convergence and correctness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "react reactive resilience for critical infrastructures using graph coloring techniques")
  Member "message passing for maximum weight independent set":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "throughput optimal decentralized scheduling with single bit state feedback for a class of queueing systems")
  Member "max product for maximum weight matching convergence correctness and lp duality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient qp admm decoder for binary ldpc codes and its performance analysis")

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.IT(Information Theory)