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 "distributed cross layer optimization in wireless networks a second order approach"):
  Abstract: Due to the rapidly growing scale and heterogeneity of wireless networks, the design of distributed cross-layer optimization algorithms has received significant interest from the networking research community. So far, the standard distributed cross-layer approach in the literature is based on the first-order Lagrangian dual decomposition and the subgradient method, which suffers from a slow convergence rate. In this paper, we make the first known attempt to develop a distributed Newton's method, which is second-order and enjoys a quadratic convergence rate. However, due to the inherent interference in wireless networks, the Hessian matrix of the cross-layer problem has a non-separable structure. As a result, developing a distributed second-order algorithm is far more difficult than its counterpart for wireline networks. Our main contributions in this paper are two-fold: i) For a special network setting where all links mutually interfere, we derive closed-form expressions for the Hessian inverse, which further yield a distributed Newton's method; ii) For general wireless networks where the interference relationships are arbitrary, we propose a double matrix-splitting scheme, which also leads to a distributed Newton's method. Collectively, these results create a new theoretical framework for distributed cross-layer optimization in wireless networks. More importantly, our work contributes to a potential second-order paradigm shift in wireless networks optimization theory.

Includes members:
  Member "a distributed newton approach for joint multi hop routing and flow control theory and algorithm":
    also connected through other hyperedges:
      (none)
  Member "distributed large scale network utility maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pareto optimal nash equilibrium in capacity allocation game for self managed networks")
  Member "gaussian belief propagation theory and application":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "polynomial linear programming with gaussian belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed intelligent illumination control in the context of probabilistic graphical models")

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.NI(Networking and Internet Architecture)