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: "background subtraction for online calibration of baseline rss in rf sensing networks"
  Abstract: Radio frequency (RF) sensing networks are a class of wireless sensor networks (WSNs) which use RF signals to accomplish tasks such as passive device-free localization and tracking. The algorithms used for these tasks usually require access to measurements of baseline received signal strength (RSS) on each link. However, it is often impossible to collect this calibration data (measurements collected during an offline calibration period when the region of interest is empty of targets). We propose adapting background subtraction methods from the field of computer vision to estimate baseline RSS values from measurements taken while the system is online and obstructions may be present. This is done by forming an analogy between the intensity of a background pixel in an image and the baseline RSS value of a WSN link and then translating the concepts of temporal similarity, spatial similarity and spatial ergodicity which underlie specific background subtraction algorithms to WSNs. Using experimental data, we show that these techniques are capable of estimating baseline RSS values with enough accuracy that RF tomographic tracking can be carried out in a variety of different environments without the need for a calibration period.

Connected through hyperedge HE-1 (induced by source paper "never use labels signal strength based bayesian device free localization in changing environments"):
  with members:
    - "hidden markov estimation of bistatic range from cluttered ultra wideband impulse responses"

Connected through hyperedge HE-2 (induced by source paper "detector based radio tomographic imaging"):
  with members:
    - "radio tomography for roadside surveillance"
    - "rti goes wild radio tomographic imaging for outdoor people detection and localization"
    - "multiple target tracking with rf sensor networks"

Connected through hyperedge HE-3 (induced by source paper "device free localization using received signal strength measurements in radio frequency network"):
  with members:
    - "drti directional radio tomographic imaging"
    - "radio tomography for roadside surveillance"
    - "multichannel rss based device free localization with wireless sensor network"
    - "dial it in rotating rf sensors to enhance radio tomography"
    - "multiple target tracking with rf sensor networks"

Connected through hyperedge HE-4 (induced by source paper "multichannel rss based device free localization with wireless sensor network"):
  with members:
    - "radio tomography for roadside surveillance"
    - "multiple target tracking with rf sensor networks"

Connected through hyperedge HE-5 (induced by source paper "dial it in rotating rf sensors to enhance radio tomography"):
  with members:
    - "breathfinding a wireless network that monitors and locates breathing in a home"
    - "multiple target tracking with rf sensor networks"
    - "a multi scale spatial model for rss based device free localization"
    - "joint ultra wideband and signal strength based through building tracking for tactical operations"

Connected through hyperedge HE-6 (induced by source paper "breathfinding a wireless network that monitors and locates breathing in a home"):
  with members:
    - "monitoring breathing via signal strength in wireless networks"

Connected through hyperedge HE-7 (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"
    - "nuzzer a large scale device free passive localization system for wireless environments"

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)