human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications"):
  Abstract: This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-betagamma (ABG) model, the close-in (CI) free-space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models has been recently studied for use in standards bodies such as 3rd Generation Partnership Project (3GPP) and for use in the design of fifth-generation wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here, we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement data sets from 2 to 73 GHz over distances ranging from 4 to 1238 m. A series of sensitivity analyses of the three models shows that the physically based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity tests across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1-m reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification — by replacing a floating non-physically based constant with a frequency-dependent constant that represents free-space path loss in the first meter of propagation. This paper shows this subtle change does not change the mathematical form of existing ITU/3GPP models and offers much easier analysis, intuitive appeal, better model parameter stability, and better accuracy in sensitivity tests over a vast range of microwave and mmWave frequencies, scenarios, and distances, while using a simpler model with fewer parameters. This material is based upon work supported by the NYU WIRELESS Industrial Affiliates: ATT tsr@nyu.edu). T. A. Thomas and A. Ghosh are with Nokia, Arlington Heights, IL 60004, USA (e-mail: timothy.thomas@nokia.com; amitava.ghosh@nokia.com). H. Nguyen and I. Rodriguez are with Aalborg University, Aalborg 9220, Denmark (e-mail: huan.nguyen.ext@nokia.com; irl@es.aau.dk). I. Z. Kovacs is with Nokia, Aalborg 9220, Denmark (e-mail: istvan.kovacs@nokia.com). O. Koymen and A. Partyka are with Qualcomm RD apartyka@qti.qualcomm.com).

Includes members:
  Member "5g 3gpp like channel models for outdoor urban microcellular and macrocellular environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "suburban fixed wireless access channel measurements and models at 28 ghz for 90 outdoor coverage")
  Member "a prediction study of path loss models from 2 73 5 ghz in an urban macro environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models")
  Member "indoor 5g 3gpp like channel models for office and shopping mall environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a combined stochastic and physical framework for modeling indoor 5g millimeter wave propagation")
  Member "propagation path loss models for 5g urban micro and macro cellular scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed learning for channel allocation over a shared spectrum")
  Member "local multipath model parameters for generating 5g millimeter wave 3gpp like channel impulse response":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "millimeter wave extended nyusim channel model for spatial consistency")
  Member "millimeter wave distance dependent large scale propagation measurements and path loss models for outdoor and indoor 5g systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "path loss and directional gain measurements at 28 ghz for non line of sight coverage of indoors with corridors")
  Member "path loss shadow fading and line of sight probability models for 5g urban macro cellular scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the spectral efficiency for massive mimo systems with imperfect spacial covariance information")
  Member "probabilistic omnidirectional path loss models for millimeter wave outdoor communications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "early acceptance matching game for user association in 5g cellular hetnet")

Question: Please tell me which class the source paper 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.IT(Information Theory)