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: "location aided fast distributed consensus in wireless networks"
  Abstract: Existing works on distributed consensus explore linear iterations based on reversible Markov chains, which contribute to the slow convergence of the algorithms. It has been observed that by overcoming the diffusive behavior of reversible chains, certain nonreversible chains lifted from reversible ones mix substantially faster than the original chains. In this paper, the idea of Markov chain lifting is studied to accelerate the convergence of distributed consensus, and two general pseudoalgorithms are presented. These pseudoalgorithms are then instantiated through a class of location-aided distributed averaging (LADA) algorithms for wireless networks, where nodes' coarse location information is used to construct nonreversible chains that facilitate distributed computing and cooperative processing. Our first LADA algorithm is designed for grid networks; for a k × k grid network, it achieves an e-averaging time of O(k log(e-1)). Based on this algorithm, in a wireless network with transmission range r, an e-averaging time of O(r-1 log(e-1)) can be attained through a centralized algorithm. Subsequently, a distributed LADA algorithm is presented, achieving the same scaling law in averaging time as the centralized scheme in wireless networks for all r satisfying the connectivity requirement; the constructed chain also attains the optimal scaling law in terms of an important mixing metric, the fill time, in its class. Finally, a cluster-based LADA algorithm is proposed, which, requiring no central coordination, provides the additional benefit of reduced message complexity compared with the distributed LADA algorithm.

Connected through hyperedge HE-1 (induced by source paper "convergence speed of the consensus algorithm with interference and sparse long range connectivity"):
  with members:
    - "sensor networks with random links topology design for distributed consensus"
    - "distance distributions in finite uniformly random networks theory and applications"
    - "network based consensus averaging with general noisy channels"
    - "geographic gossip efficient aggregation for sensor networks"

Connected through hyperedge HE-2 (induced by source paper "gossip algorithms for distributed signal processing"):
  with members:
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "optimization and analysis of distributed averaging with short node memory"
    - "distributed lossy averaging"
    - "polynomial filtering for fast convergence in distributed consensus"
    - "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-3 (induced by source paper "multiscale gossip for efficient decentralized averaging in wireless packet networks"):
  with members:
    - "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"
    - "polynomial filtering for fast convergence in distributed consensus"
    - "geographic gossip efficient averaging for sensor networks"
    - "order optimal consensus through randomized path averaging"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-4 (induced by source paper "polynomial filtering for fast convergence in distributed consensus"):
  with members:
    - "geographic gossip efficient averaging for sensor networks"
    - "sensor networks with random links topology design for distributed consensus"

Connected through hyperedge HE-5 (induced by source paper "order optimal consensus through randomized path averaging"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"
    - "geographic gossip efficient aggregation for sensor networks"
    - "gossiping with multiple messages"
    - "information dissemination via gossip applications to averaging and coding"

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)