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: "distributed large scale network utility maximization"
  Abstract: Recent work by Zymnis et al. proposes an efficient primal-dual interior-point method, using a truncated Newton method, for solving the network utility maximization (NUM) problem. This method has shown superior performance relative to the traditional dual-decomposition approach. Other recent work by Bickson et al. shows how to compute efficiently and distributively the Newton step, which is the main computational bottleneck of the Newton method, utilizing the Gaussian belief propagation algorithm. In the current work, we combine both approaches to create an efficient distributed algorithm for solving the NUM problem. Unlike the work of Zymnis, which uses a centralized approach, our new algorithm is easily distributed. Using an empirical evaluation we show that our new method outperforms previous approaches, including the truncated Newton method and dual-decomposition methods. As an additional contribution, this is the first work that evaluates the performance of the Gaussian belief propagation algorithm vs. the preconditioned conjugate gradient method, for a large scale problem.

Connected through hyperedge HE-1 (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"
    - "gaussian belief propagation theory and application"
    - "polynomial linear programming with gaussian belief propagation"

Connected through hyperedge HE-2 (induced by source paper "pareto optimal nash equilibrium in capacity allocation game for self managed networks"):
  with members:
    - "bottleneck congestion games with logarithmic price of anarchy"

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:
    - "gaussian belief propagation theory and application"
    - "polynomial linear programming with gaussian belief propagation"

Connected through hyperedge HE-4 (induced by source paper "distributive network utility maximization over time varying fading channels"):
  with members:
    - "distributive power control algorithm for multicarrier interference network over time varying fading channels tracking performance analysis and optimization"

Connected through hyperedge HE-5 (induced by source paper "fault identification via non parametric belief propagation"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "a low density lattice decoder via non parametric belief propagation"
    - "gaussian belief propagation based multiuser detection"
    - "iterative hard thresholding for compressed sensing"
    - "message passing decoding of lattices using gaussian mixtures"
    - "bayesian compressive sensing via belief propagation"
    - "low density lattice codes"
    - "belief propagation based multi user detection"

Connected through hyperedge HE-6 (induced by source paper "distributed sensor selection using a truncated newton method"):
  with members:
    - "gaussian belief propagation theory and application"
    - "polynomial linear programming with gaussian belief propagation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "polynomial linear programming with 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)