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: "radio tomography for roadside surveillance"
  Abstract: Radio tomographic imaging (RTI) has recently been proposed for tracking object location via radio waves without requiring the objects to transmit or receive radio signals. The position is extracted by inferring which voxels are obstructing a subset of radio links in a dense wireless sensor network. This paper proposes a variety of modeling and algorithmic improvements to RTI for the scenario of roadside surveillance. These include the use of a more physically motivated weight matrix, a method for mitigating negative (aphysical) data due to noisy observations, and a method for combining frames of a moving vehicle into a single image. The proposed approaches are used to show improvement in both imaging (useful for human-in-the-loop target recognition) and automatic target recognition in a measured data set.

Connected through hyperedge HE-1 (induced by source paper "detector based radio tomographic imaging"):
  with members:
    - "rti goes wild radio tomographic imaging for outdoor people detection and localization"
    - "multiple target tracking with rf sensor networks"
    - "background subtraction for online calibration of baseline rss in rf sensing networks"

Connected through hyperedge HE-2 (induced by source paper "device free localization using received signal strength measurements in radio frequency network"):
  with members:
    - "drti directional radio tomographic imaging"
    - "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"
    - "background subtraction for online calibration of baseline rss in rf sensing networks"

Connected through hyperedge HE-3 (induced by source paper "rti goes wild radio tomographic imaging for outdoor people detection and localization"):
  with members:
    - "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:
    - "multiple target tracking with rf sensor networks"
    - "background subtraction for online calibration of baseline rss in rf sensing 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)