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 filtering for fast convergence in distributed consensus"
  Abstract: In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. The convergence rate of such distributed algorithms typically depends on the network topology and the weights given to the edges between neighboring sensors, as described by the network matrix. In this paper, we propose to accelerate the convergence rate for given network matrices by the use of polynomial filtering algorithms. The main idea of the proposed methodology is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the convergence rate. Such an algorithm is equivalent to periodic updates in each of the sensors by aggregating a few of its previous estimates. We formulate the computation of the coefficients of the optimal polynomial as a semidefinite program that can be efficiently and globally solved for both static and dynamic network topologies. We finally provide simulation results that demonstrate the effectiveness of the proposed solutions in accelerating the convergence of distributed consensus averaging problems.

Connected through hyperedge HE-1 (induced by source paper "average consensus by graph filtering new approach explicit convergence rate and optimal design"):
  with members:
    - "discrete signal processing on graphs sampling theory"
    - "discrete signal processing on graphs frequency analysis"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "consensus of multi agent systems with general linear and lipschitz nonlinear dynamics using distributed adaptive protocols"
    - "discrete signal processing on graphs"
    - "bilateral filter graph spectral interpretation and extensions"
    - "chebyshev polynomials in distributed consensus applications"

Connected through hyperedge HE-2 (induced by source paper "spectral statistics of lattice graph structured non uniform percolations"):
  with members:
    - "signal recovery on graphs variation minimization"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-3 (induced by source paper "spectral statistics of lattice graph percolation models"):
  with members:
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-4 (induced by source paper "accelerating consensus by spectral clustering and polynomial filters"):
  with members:
    - "finite time consensus using stochastic matrices with positive diagonals"
    - "chebyshev polynomials in distributed consensus applications"

Connected through hyperedge HE-5 (induced by source paper "chebyshev polynomials in distributed consensus applications"):
  with members:
    - "optimization and analysis of distributed averaging with short node memory"

Connected through hyperedge HE-6 (induced by source paper "gossip algorithms for distributed signal processing"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "optimization and analysis of distributed averaging with short node memory"
    - "distributed lossy averaging"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "the impact of mobility on gossip algorithms"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributed and recursive parameter estimation in parametrized linear state space models"

Connected through hyperedge HE-7 (induced by source paper "multiscale gossip for efficient decentralized averaging in wireless packet networks"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "gossip algorithms for distributed signal processing"
    - "optimization and analysis of distributed averaging with short node memory"
    - "distributed averaging via lifted markov chains"
    - "hierarchical routing over dynamic wireless networks"
    - "geographic gossip efficient averaging for sensor networks"
    - "order optimal consensus through randomized path averaging"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

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)