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: "cramer rao bounds for joint rss doa based primary user localization in cognitive radio networks"
  Abstract: Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.

Connected through hyperedge HE-1 (induced by source paper "peregrine network localization and navigation with scalable inference and efficient operation"):
  with members:
    - "pricing mechanisms for crowd sensed spatial statistics based radio mapping"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"
    - "distributed maximum likelihood sensor network localization"
    - "sigma point belief propagation"
    - "fundamental limits of wideband localization part ii cooperative networks"
    - "fundamental limits of wideband localization part i a general framework"

Connected through hyperedge HE-2 (induced by source paper "location verification systems under spatially correlated shadowing"):
  with members:
    - "location verification systems for vanets in rician fading channels"
    - "map aware models for indoor wireless localization systems an experimental study"
    - "location spoofing detection for vanets by a single base station in rician fading channels"
    - "optimal information theoretic wireless location verification"
    - "an information theoretic location verification system for wireless networks"

Connected through hyperedge HE-3 (induced by source paper "accuracy of range based cooperative localization in wireless sensor networks a lower bound analysis"):
  with members:
    - "fundamental limits of wideband localization part ii cooperative 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.PF(Performance)