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: "nuzzer a large scale device free passive localization system for wireless environments"
  Abstract: The widespread usage of wireless local area networks and mobile devices has fostered the interest in localization systems for wireless environments. The majority of research in the context of wireless-based localization systems has focused on device-based active localization, in which a device is attached to tracked entities. Recently, device-free passive localization (DfP) has been proposed where the tracked entity is neither required to carry devices nor participate actively in the localization process. DfP systems are based on the fact that RF signals are affected by the presence of people and objects in the environment. The DfP concept enables a wide range of applications including intrusion detection and tracking, border protection, and smart buildings automation. Previous studies have focused on small areas with direct line of sight and/or controlled environments. In this paper, we present the design, implementation and analysis of Nuzzer, a large-scale device-free passive localization system for real environments. #R##N#Without any additional hardware, it makes use of the already installed wireless data networks to monitor and process changes in the received signal strength (RSS) transmitted from access points at one or more monitoring points. We present probabilistic techniques for DfP localization and evaluate their performance in a typical office building, rich in multipath, with an area of 1500 square meters. Our results show that the Nuzzer system gives device-free location estimates with less than 2 meters median distance error using only two monitoring laptops and three access points. This indicates the suitability of Nuzzer to a large number of application domains.

Connected through hyperedge HE-1 (induced by source paper "through wall person localization using transceivers in motion"):
  with members:
    - "channel sounding for the masses low complexity gnu 802 11b channel impulse response estimation"
    - "hidden markov estimation of bistatic range from cluttered ultra wideband impulse responses"

Connected through hyperedge HE-2 (induced by source paper "an analysis of device free and device based wifi localization systems"):
  with members:
    - "spot an accurate and efficient multi entity device free wlan localization system"
    - "rasid a robust wlan device free passive motion detection system"
    - "aroma automatic generation of radio maps for localization systems"

Connected through hyperedge HE-3 (induced by source paper "a multi scale spatial model for rss based device free localization"):
  with members:
    - "a gentle introduction to the kernel distance"

Connected through hyperedge HE-4 (induced by source paper "robust estimators for variance based device free localization and tracking"):
  with members:
    - "dial it in rotating rf sensors to enhance radio tomography"
    - "multiple target tracking with rf sensor networks"
    - "background subtraction for online calibration of baseline rss in rf sensing networks"

Connected through hyperedge HE-5 (induced by source paper "people sensing spatial characteristics of rf sensor networks"):
  with members:
    - "through wall tracking using variance based radio tomography 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.NI(Networking and Internet Architecture)