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: "differential evolution algorithm aided turbo channel estimation and multi user detection for g fast systems in the presence of fext"
  Abstract: The ever-increasing demand for broadband Internet access has motivated the further development of the digital subscriber line to the G.fast standard in order to expand its operational band from 106 to 212 MHz. Conventional far-end crosstalk (FEXT)-based cancellers falter in the upstream transmission of this emerging G.fast system. In this paper, we propose a novel differential evolution algorithm (DEA)-aided turbo channel estimation (CE) and a multi-user detection (MUD) scheme for the G.fast upstream, including the frequency band up to 212 MHz, which is capable of approaching the optimal Cramer–Rao lower bound of the channel estimate, whilst approaching the optimal maximum likelihood MUD’s performance associated with perfect channel state information and, yet, only imposing about 5% of its computational complexity. Explicitly, the turbo concept is exploited by iteratively exchanging information between the continuous value-based DEA-assisted channel estimator and the discrete value-based DEA MUD. Our extensive simulations show that 18-dB normalized mean square error gain is attained by the channel estimator and 10-dB signal-to-noise ratio gain can be achieved by the MUD upon exploiting this iteration gain. We also quantify the influence of the CE error, the copper length, and the impulse noise. This paper demonstrates that the proposed DEA-aided turbo CE and MUD scheme is capable of offering near-capacity performance at an affordable complexity for the emerging G.fast systems.

Connected through hyperedge HE-1 (induced by source paper "boosting fronthaul capacity global optimization of power sharing for centralized radio access network"):
  with members:
    - "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "multivariate fronthaul quantization for downlink c ran"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access 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.IT(Information Theory)