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: "decentralized random walk based data collection in networks"
  Abstract: We analyze a decentralized random walk-based algorithm for data collection at the sink in a multi-hop sensor network. Our algorithm, Random-Collect, which involves data packets being passed to random neighbors in the network according to a random walk mechanism, requires no configuration and incurs no routing overhead. To analyze this method, we model the data generation process as independent Bernoulli arrivals at the source nodes. We analyze both latency and throughput in this setting, providing a theoretical lower bound for the throughput and a theoretical upper bound for the latency. The main contribution of our paper, however, is the throughput result: we show that the rate at which our algorithm can collect data depends on the spectral gap of the given random walk's transition matrix. In particular, for the simple random walk, we show that the rate also depends on the maximum and minimum degrees of the graph modeling the network. For latency, we show that the time taken to collect data not only depends on the worst-case hitting time of the given random walk but also depends on the data arrival rate. In fact, our latency bound reflects the data rate-latency trade-off i.e., in order to achieve a higher data rate we need to compromise on latency and vice-versa. We also discuss some examples that demonstrate that our lower bound on the data rate is optimal up to constant factors, i.e., there exists a network topology and sink placement for which the maximum stable data rate is just a constant factor above our lower bound.

Connected through hyperedge HE-1 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "sampling random spanning trees faster than matrix multiplication"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "computing maximum flow with augmenting electrical flows"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "fast generation of random spanning trees and the effective resistance metric"

Connected through hyperedge HE-2 (induced by source paper "random walk based in network computation of arbitrary functions"):
  with members:
    - "on coalescence time in graphs when is coalescing as fast as meeting"
    - "on the maximum rate of networked computation in a capacitated network"
    - "optimal embedding of functions for in network computation complexity analysis and algorithms"
    - "coalescing random walks and voting on connected graphs"
    - "tight bounds for randomized load balancing on arbitrary network topologies"
    - "towards a queueing based framework for in network function computation"

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.NI(Networking and Internet Architecture)