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 averaging via lifted markov chains"
  Abstract: Motivated by applications of distributed linear estimation, distributed control and distributed optimization, we consider the question of designing linear iterative algorithms for computing the average of numbers in a network. Specifically, our interest is in designing such an algorithm with the fastest rate of convergence given the topological constraints of the network. As the main result of this paper, we design an algorithm with the fastest possible rate of convergence using a non-reversible Markov chain on the given network graph. We construct such a Markov chain by transforming the standard Markov chain, which is obtained using the Metropolis-Hastings method. We call this novel transformation pseudo-lifting. We apply our method to graphs with geometry, or graphs with doubling dimension. Specifically, the convergence time of our algorithm (equivalently, the mixing time of our Markov chain) is proportional to the diameter of the network graph and hence optimal. As a byproduct, our result provides the fastest mixing Markov chain given the network topological constraints, and should naturally find their applications in the context of distributed optimization, estimation and control.

Connected through hyperedge HE-1 (induced by source paper "consensus over random graph processes network borel cantelli lemmas for almost sure convergence"):
  with members:
    - "finite time convergent gossiping"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"
    - "fast distributed algorithms for computing separable functions"

Connected through hyperedge HE-2 (induced by source paper "on the role of mobility for multimessage gossip"):
  with members:
    - "information spreading in dynamic graphs"
    - "tight bounds on information dissemination in sparse mobile networks"
    - "information spreading in stationary markovian evolving graphs"
    - "analyzing network coding gossip made easy"
    - "the impact of mobility on gossip algorithms"
    - "gossiping with multiple messages"
    - "fast distributed algorithms for computing separable functions"

Connected through hyperedge HE-3 (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"
    - "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"

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)