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: "a unifying bayesian optimization framework for radio frequency localization"
  Abstract: We consider the problem of estimating an RF-device's location based on observations, such as received signal strength, from a set of transmitters with known locations. We survey the literature on this problem, showing that previous authors have considered implicitly or explicitly various metrics. We present a novel Bayesian framework that unifies these works and shows how to optimize the location estimation with respect to a given metric. We demonstrate how the framework can incorporate a general class of algorithms, including both model-based methods and data-driven algorithms such fingerprinting. This is illustrated by re-deriving the most popular algorithms within this framework. When used with a data-driven approach, our framework has cognitive self-improving properties in that it provably improves with increasing data compared to traditional methods. Furthermore, we propose using the error-CDF as a unified way of comparing algorithms based on two methods: (i) stochastic dominance, and (ii) an upper bound on error-CDFs. We prove that an algorithm that optimizes any distance based cost function is not stochastically dominated by any other algorithm. This suggests that in lieu of the search for a universally best localization algorithm, the community should focus on finding the best algorithm for a given well-defined objective.

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)