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: "linear large scale mimo data detection for 5g multi carrier waveform candidates"
  Abstract: Fifth generation (5G) wireless systems are expected to combine emerging transmission technologies, such as large-scale multiple-input multiple-output (MIMO) and non-orthogonal multi-carrier waveforms, to improve the spectral efficiency and to reduce out-of-band (OOB) emissions. This paper investigates the efficacy of two promising multi-carrier waveforms that reduce OOB emissions in combination with large-scale MIMO, namely filter bank multi-carrier (FBMC) and generalized frequency division multiplexing (GFDM). We develop novel, low-complexity data detection algorithms for both of these waveforms. We investigate the associated performance/complexity trade-offs in the context of large-scale MIMO, and we study the peak-to-average power ratio (PAPR). Our results show that reducing the OOB emissions with FBMC and GFDM leads to higher computational complexity and PAPR compared to that of orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency division multiple access (SC-FDMA).

Connected through hyperedge HE-1 (induced by source paper "frequency domain decoupling for mimo gfdm spatial multiplexing"):
  with members:
    - "matrix characterization for gfdm low complexity mmse receivers and optimal filters"

Connected through hyperedge HE-2 (induced by source paper "decentralized baseband processing for massive mu mimo systems"):
  with members:
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"

Connected through hyperedge HE-3 (induced by source paper "high throughput data detection for massive mu mimo ofdm using coordinate descent"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "scaling up mimo opportunities and challenges with very large arrays"

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)