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 "higher dimensional consensus learning in large scale networks"):
  Abstract: The paper considers higher dimensional consensus (HDC). HDC is a general class of linear distributed algorithms for large-scale networks that generalizes average-consensus and includes other interesting distributed algorithms, like sensor localization, leader-follower algorithms in multiagent systems, or distributed Jacobi algorithm. In HDC, the network nodes are partitioned into ?anchors?, nodes whose states are fixed over the HDC iterations, and ?sensors?, nodes whose states are updated by the algorithm. The paper starts by briefly considering what we call the forward problem by presenting the conditions for HDC to converge, the limiting state to which it converges, and what is its convergence rate. The main focus of the paper is the inverse or design problem, i.e., learning the weights or parameters of the HDC so that the algorithm converges to a desired prespecified state. This generalizes the well-known problem of designing the weights in average-consensus. We pose learning as a constrained nonconvex optimization problem that we cast in the framework of multiobjective optimization (MOP) and to which we apply Pareto optimality. We derive the solution to the learning problem by proving relevant properties satisfied by the MOP solutions and by the Pareto front. Finally, the paper shows how the MOP approach leads to interesting tradeoffs (speed of convergence versus performance) arising in resource constrained networks. Simulation studies illustrate our approach for a leader-follower architecture in multiagent systems.

Includes members:
  Member "diland an algorithm for distributed sensor localization with noisy distance measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "localization in internets of mobile agents a linear approach")
  Member "distributed sensor localization in random environments using minimal number of anchor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed joint sensor registration and multitarget tracking via sensor network")
  Member "sensor networks with random links topology design for distributed consensus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matcha speeding up decentralized sgd via matching decomposition sampling")
  Member "distributing the kalman filter for large scale systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coded distributed tracking")
  Member "distributed consensus algorithms in sensor networks link failures and channel noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multitask diffusion adaptation over networks with common latent representations")
  Member "topology for distributed inference on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed detection in ad hoc networks through quantized consensus")

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)