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 "mobile millimeter wave channel acquisition tracking and abrupt change detection"):
  Abstract: Millimeter wave provides a promising approach for meeting the ever-growing traffic demand in next generation wireless networks. It is crucial to obtain relatively accurate channel state information so that beamforming/combining can be performed to compensate for severe path loss in this band. In contrast to lower frequencies, a typical mobile millimeter wave channel consists of a few dominant paths. It is generally sufficient to estimate the path gains, angles of departure (AoD), and angles of arrival (AoA) of those paths. In this paper, multiple transmit and receive antennas and beamforming with a single baseband processing chain are assumed. We propose a framework for estimating millimeter wave channels with intermittent abrupt changes (e.g., blockage or emergence of dominant paths) and slow variations of AoDs and AoAs. The solution consists of three components: tracking of the slow channel variations, detection of abrupt changes, followed by (re-)acquisition of channel (and back to the tracking stage). For acquisition, we formulate a least squares problem and find its solution based on the Levenberg-Marquardt algorithm. To track slow variations of AoDs and AoAs, we propose a new approach using Kalman filtering. Finally, an algorithm based on a likelihood test is devised for detecting abrupt changes. Simulation results show that, with moderate signal-to-noise ratios, the proposed scheme can achieve more than 8 dB higher estimation accuracy than several other methods using the same number of pilots.

Includes members:
  Member "28 ghz millimeter wave ultrawideband small scale fading models in wireless channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast accurate beam and channel tracking for two dimensional phased antenna array")
  Member "mimo channel modeling and capacity analysis for 5g millimeter wave wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast harq over finite blocklength codes a technique for low latency reliable communication")
  Member "tracking angles of departure and arrival in a mobile millimeter wave channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tracking position and orientation through millimeter wave lens mimo in 5g systems")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "spatially sparse precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "millimeter wave channel modeling and cellular capacity evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach")
  Member "five disruptive technology directions for 5g":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")

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)