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: "polynomial linear programming with gaussian belief propagation"
  Abstract: Interior-point methods are state-of-the-art algorithms for solving linear programming (LP) problems with polynomial complexity. Specifically, the Karmarkar algorithm typically solves LP problems in time O(n3.5), where n is the number of unknown variables. Karmarkar's celebrated algorithm is known to be an instance of the log-barrier method using the Newton iteration. The main computational overhead of this method is in inverting the Hessian matrix of the Newton iteration. In this contribution, we propose the application of the Gaussian belief propagation (GaBP) algorithm as part of an efficient and distributed LP solver that exploits the sparse and symmetric structure of the Hessian matrix and avoids the need for direct matrix inversion. This approach shifts the computation from realm of linear algebra to that of probabilistic inference on graphical models, thus applying GaBP as an efficient inference engine. Our construction is general and can be used for any interior-point algorithm which uses the Newton method, including non-linear program solvers.

Connected through hyperedge HE-1 (induced by source paper "distributed intelligent illumination control in the context of probabilistic graphical models"):
  with members:
    - "visible light communications based indoor positioning via compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "distributed cross layer optimization in wireless networks a second order approach"):
  with members:
    - "a distributed newton approach for joint multi hop routing and flow control theory and algorithm"
    - "distributed large scale network utility maximization"
    - "gaussian belief propagation theory and application"

Connected through hyperedge HE-3 (induced by source paper "a distributed newton approach for joint multi hop routing and flow control theory and algorithm"):
  with members:
    - "distributed large scale network utility maximization"
    - "gaussian belief propagation theory and application"

Connected through hyperedge HE-4 (induced by source paper "discrete geometric analysis of message passing algorithm on graphs"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation propagation for approximate bayesian inference"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "graph zeta function in the bethe free energy and loopy belief propagation"
    - "approximate inference on planar graphs using loop calculus and belief propagation"
    - "gaussian belief propagation solver for systems of linear equations"
    - "belief propagation and beyond for particle tracking"
    - "pseudo codewords of cycle codes via zeta functions"

Connected through hyperedge HE-5 (induced by source paper "fixing convergence of gaussian belief propagation"):
  with members:
    - "a gaussian belief propagation solver for large scale support vector machines"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "distributed kalman filter via gaussian belief propagation"
    - "belief propagation based multi user detection"

Connected through hyperedge HE-6 (induced by source paper "distributed large scale network utility maximization"):
  with members:
    - "fixing convergence of gaussian belief propagation"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"

Connected through hyperedge HE-7 (induced by source paper "distributed sensor selection using a truncated newton method"):
  with members:
    - "distributed large scale network utility maximization"
    - "gaussian belief propagation theory and application"

Connected through hyperedge HE-8 (induced by source paper "gaussian belief propagation theory and application"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "distributed large scale network utility maximization"
    - "a gaussian belief propagation solver for large scale support vector machines"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "distributed kalman filter via gaussian belief propagation"

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.IT(Information Theory)