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: "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
  Abstract: In this paper, we propose a MIMO receiver algorithm that exploits {\em channel hardening} that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the ${\bf H}^H{\bf H}$ matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix ${\bf H}$ increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes ${\bf H}^T{\bf H}{\bf x}$, where ${\bf x}$ is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the ${\bf H}^T{\bf H}$ matrix. We also propose a simple estimation scheme which directly obtains an estimate of ${\bf H}^T{\bf H}$ (instead of an estimate of ${\bf H}$), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the {\em channel hardening-exploiting message passing (CHEMP)} receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of ${\bf H}$. We also present a convergence analysis of the proposed MPD algorithm. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

Connected through hyperedge HE-1 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks"):
  with members:
    - "pilot spoofing attack by multiple eavesdroppers"
    - "multiple antennas secure transmission under pilot spoofing and jamming attack"
    - "active attack on user load achieving pilot design in massive mimo networks"
    - "user load analysis and pilot sequence design for multi cell massive mimo networks"
    - "the world s first real time testbed for massive mimo design implementation and validation"
    - "multi cell multiuser massive mimo networks user capacity analysis and pilot design"
    - "massive mimo pilot retransmission strategies for robustification against jamming"
    - "subverting massive mimo by smart jamming"

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"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "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 354mb s 0 37mm 2 151mw 32 user 256 qam near map soft input soft output massive mu mimo data detector in 28nm cmos"):
  with members:
    - "optimality of large mimo detection via approximate message passing"
    - "massive mimo for next generation wireless systems"

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"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-5 (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"
    - "fifty years of mimo detection the road to large scale mimos"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-6 (induced by source paper "large multiuser mimo detection algorithms and architectures"):
  with members:
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "1 bit massive mu mimo precoding in vlsi"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "linear precoding with low resolution dacs for massive mu mimo ofdm downlink"
    - "analysis of one bit quantized precoding for the multiuser massive mimo downlink"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-7 (induced by source paper "pilot power allocation through user grouping in multi cell massive mimo systems"):
  with members:
    - "power allocation schemes for multicell massive mimo systems"
    - "channel estimation in massive mimo systems"
    - "uplink pilot and data power control for single cell massive mimo systems with mrc"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"
    - "massive mimo multicasting in noncooperative cellular networks"

Connected through hyperedge HE-8 (induced by source paper "massive mimo for drone communications case studies and future directions"):
  with members:
    - "convolutionally coded snr adaptive transmission for low latency communications"
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "mobile networks connected drones field trials simulations and design insights"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a telecom perspective on the internet of drones from lte advanced to 5g"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
    - "security privacy and safety evaluation of dynamic and static fleets of drones"

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)