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: "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
  Abstract: Large-scale (or massive) multiple-input multiple-out put (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose-to the best of our knowledge-the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.

Connected through hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo"):
  with members:
    - "gaussian message passing for overloaded massive mimo noma"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
    - "decentralized baseband processing for massive mu mimo systems"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "performance trade offs in multi processor approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "deep learning aided tabu search detection for large mimo systems"):
  with members:
    - "learning to detect"
    - "detection algorithms for communication systems using deep learning"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-3 (induced by source paper "a proximal jacobian admm approach for fast massive mimo signal detection in low latency communications"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a low complexity detection algorithm based on alternating minimization"
    - "two stage lasso admm signal detection algorithm for large scale mimo"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-4 (induced by source paper "optimal data detection in large mimo"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "compressive phase retrieval via generalized approximate message passing"
    - "does ell_p minimization outperform ell_1 minimization"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "efficient dsp and circuit architectures for massive mimo state of the art and future directions"):
  with members:
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "out of band radiation from antenna arrays clarified"
    - "spatial characteristics of distortion radiated from antenna arrays with transceiver nonlinearities"
    - "decentralized baseband processing for massive mu mimo systems"
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "on out of band emissions of quantized precoding in massive mu mimo ofdm"
    - "achievable uplink rates for massive mimo with coarse quantization"

Connected through hyperedge HE-6 (induced by source paper "data detection in single user massive mimo using re transmissions"):
  with members:
    - "near capacity signaling over fading channels using coherent turbo coded ofdm and massive mimo"
    - "doubly massive mmwave mimo systems using very large antenna arrays at both transmitter and receiver"
    - "a multiband ofdma heterogeneous network for millimeter wave 5g wireless applications"
    - "coherent detection of turbo coded ofdm signals transmitted through frequency selective rayleigh fading channels with receiver diversity and increased throughput"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-7 (induced by source paper "1 bit massive mu mimo precoding in vlsi"):
  with members:
    - "decentralized baseband processing for massive mu mimo systems"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "massive mu mimo ofdm downlink with one bit dacs and linear precoding"
    - "nonlinear 1 bit precoding for massive mu mimo with higher order modulation"
    - "minimum ber precoding in 1 bit massive mimo systems"
    - "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"
    - "performance of the wideband massive uplink mimo with one bit adcs"

Connected through hyperedge HE-8 (induced by source paper "vlsi design of a nonparametric equalizer for massive mu mimo"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "massive mimo how many antennas do we need"

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)