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 "a data aided channel estimation scheme for decoupled systems in heterogeneous networks"):
  Abstract: Uplink/downlink (UL/DL) decoupling promises more flexible cell association and higher throughput in heterogeneous networks (HetNets), however, it hampers the acquisition of DL channel state information (CSI) in time-division-duplex (TDD) systems due to different base stations (BSs) connected in UL/DL. In this paper, we propose a novel data-aided (DA) channel estimation scheme to address this problem by utilizing decoded UL data to exploit CSI from received UL data signal in decoupled HetNets where a massive multiple-input multiple-output BS and dense small cell BSs are deployed. We analytically estimate BER performance of UL decoded data, which are used to derive an approximated normalized mean square error (NMSE) expression of the DA minimum mean square error (MMSE) estimator. Compared with the conventional least square (LS) and MMSE, it is shown that NMSE performances of all estimators are determined by their signal-to-noise ratio (SNR)-like terms and there is an increment consisting of UL data power, UL data length and BER values in the SNR-like term of DA method, which suggests DA method outperforms the conventional ones in any scenarios. Higher UL data power, longer UL data length and better BER performance lead to more accurate estimated channels with DA method. Numerical results verify that the analytical BER and NMSE results are close to the simulated ones and a remarkable gain in both NMSE and DL rate can be achieved by DA method in multiple scenarios with different modulations.

Includes members:
  Member "performance impact of los and nlos transmissions in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs")
  Member "why to decouple the uplink and downlink in cellular networks and how to do it":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance of network assisted full duplex for cell free massive mimo")
  Member "analysis of the decoupled access for downlink and uplink in wireless heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic time frequency division duplex")
  Member "joint rate and sinr coverage analysis for decoupled uplink downlink biased cell associations in hetnets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spatiotemporal framework for information freshness in iot uplink networks")
  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 "massive mimo meets hetnet interference coordination through spatial blanking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non coherent detection and bit error rate for an ambient backscatter link in time selective fading")
  Member "downlink and uplink decoupling a disruptive architectural design for 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on the security and the evolution of osmotic and catalytic computing for 5g networks")
  Member "load backhaul aware decoupled downlink uplink access in 5g systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "uplink spectral efficiency analysis of decoupled access in multiuser mimo communications")

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)