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: "fifty years of mimo detection the road to large scale mimos"
  Abstract: The emerging massive/large-scale multiple-input multiple-output (LS-MIMO) systems that rely on very large antenna arrays have become a hot topic of wireless communications. Compared to multi-antenna aided systems being built at the time of this writing, such as the long-term evolution (LTE) based fourth generation (4G) mobile communication system which allows for up to eight antenna elements at the base station (BS), the LS-MIMO system entails an unprecedented number of antennas, say 100 or more, at the BS. The huge leap in the number of BS antennas opens the door to a new research field in communication theory, propagation and electronics, where random matrix theory begins to play a dominant role. Interestingly, LS-MIMOs also constitute a perfect example of one of the key philosophical principles of the Hegelian Dialectics, namely, that “quantitative change leads to qualitative change.” In this treatise, we provide a recital on the historic heritages and novel challenges facing LS-MIMOs from a detection perspective. First, we highlight the fundamentals of MIMO detection, including the nature of co-channel interference (CCI), the generality of the MIMO detection problem, the received signal models of both linear memoryless MIMO channels and dispersive MIMO channels exhibiting memory, as well as the complex-valued versus real-valued MIMO system models. Then, an extensive review of the representative MIMO detection methods conceived during the past 50 years (1965–2015) is presented, and relevant insights as well as lessons are inferred for the sake of designing complexity-scalable MIMO detection algorithms that are potentially applicable to LS-MIMO systems. Furthermore, we divide the LS-MIMO systems into two types, and elaborate on the distinct detection strategies suitable for each of them. The type-I LS-MIMO corresponds to the case where the number of active users is much smaller than the number of BS antennas, which is currently the mainstream definition of LS-MIMO. The type-II LS-MIMO corresponds to the case where the number of active users is comparable to the number of BS antennas. Finally, we discuss the applicability of existing MIMO detection algorithms in LS-MIMO systems, and review some of the recent advances in LS-MIMO detection.

Connected through hyperedge HE-1 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "model driven deep learning for physical layer communications"
    - "a model driven deep learning network for mimo detection"

Connected through hyperedge HE-2 (induced by source paper "learning to search for mimo detection"):
  with members:
    - "learning to detect"
    - "multilevel mimo detection with deep learning"
    - "two stage lasso admm signal detection algorithm for large scale mimo"
    - "a model driven deep learning network for mimo detection"
    - "learning combinatorial optimization algorithms over graphs"
    - "learning to optimize"
    - "optimality of large mimo detection via approximate message passing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel"):
  with members:
    - "cube split a structured grassmannian constellation for non coherent simo communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a new class of upper bounds on the log partition function"
    - "on the capacity of large mimo block fading channels"
    - "mimo detection for high order qam based on a gaussian tree approximation"

Connected through hyperedge HE-4 (induced by source paper "on the equivalence of semidifinite relaxations for mimo detection with general constellations"):
  with members:
    - "optimal estimation of sensor biases for asynchronous multi sensor data fusion"
    - "the equivalence of semidefinite relaxation mimo detectors for higher order qam"
    - "the diversity order of the semidefinite relaxation detector"
    - "a near maximum likelihood decoding algorithm for mimo systems based on semi definite programming"

Connected through hyperedge HE-5 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement"):
  with members:
    - "boosted kz and lll algorithms"
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "polar codes and polar lattices for independent fading channels"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"

Connected through hyperedge HE-6 (induced by source paper "probabilistic mimo symbol detection with expectation consistency approximate inference"):
  with members:
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "spatially coupled ldpc codes constructed from protographs"
    - "optimality of large mimo detection via approximate message passing"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-7 (induced by source paper "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "learning to detect"
    - "complex field trainable ista for linear and nonlinear inverse problems"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "deep learning aided projected gradient detector for massive overloaded mimo channels"
    - "a model driven deep learning network for mimo detection"
    - "deep learning for wireless physical layer opportunities and challenges"

Connected through hyperedge HE-8 (induced by source paper "unified overview of matrix monotonic optimization for mimo transceivers"):
  with members:
    - "a framework for joint design of pilot sequence and linear precoder"
    - "on weighted mse model for mimo transceiver optimization"
    - "a framework for transceiver designs for multi hop communications with covariance shaping constraints"
    - "convex separable problems with linear constraints in signal processing and communications"
    - "matrix monotonic optimization for mimo systems"
    - "robust transceiver with tomlinson harashima precoding for amplify and forward mimo relaying systems"
    - "miso capacity with per antenna power constraint"

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)