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: "on an achievable rate of large rayleigh block fading mimo channels with no csi"
  Abstract: Training-based transmission over Rayleigh block-fading multiple-input multiple-output (MIMO) channels is investigated. As a training method a combination of a pilot-assisted scheme and a biased signaling scheme is considered. The achievable rates of successive decoding (SD) receivers based on the linear minimum mean-squared error (LMMSE) channel estimation are analyzed in the large-system limit, by using the replica method under the assumption of replica symmetry. It is shown that negligible pilot information is best in terms of the achievable rates of the SD receivers in the large-system limit. The obtained analytical formulas of the achievable rates can improve the existing lower bound on the capacity of the MIMO channel with no channel state information (CSI), derived by Hassibi and Hochwald, for all SNRs. The comparison between the obtained bound and a high-SNR approximation of the channel capacity, derived by Zheng and Tse, implies that the high-SNR approximation is unreliable unless quite high SNR is considered. Energy efficiency in the low-SNR regime is also investigated in terms of the power per information bit required for reliable communication. The required minimum power is shown to be achieved at a positive rate for the SD receiver with no CSI, whereas it is achieved in the zero-rate limit for the case of perfect CSI available at the receiver. Moreover, numerical simulations imply that the presented large-system analysis can provide a good approximation for not so large systems. The results in this paper imply that SD schemes can provide a significant performance gain in the low-to-moderate SNR regimes, compared to conventional receivers based on one-shot channel estimation.

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 "superimposed pilots are superior for mitigating pilot contamination in massive mimo"):
  with members:
    - "massive mimo ten myths and one critical question"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "blind pilot decontamination"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"):
  with members:
    - "mixed adc massive mimo"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "one bit massive mimo channel estimation and high order modulations"
    - "limited feedback in multiple antenna systems with one bit quantization"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "joint channel and data estimation for large mimo systems with low precision adcs"

Connected through hyperedge HE-4 (induced by source paper "downlink performance of superimposed pilots in massive mimo systems"):
  with members:
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "blind pilot decontamination"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-5 (induced by source paper "superimposed pilots are superior for mitigating pilot contamination in massive mimo part ii performance analysis and hybrid system design"):
  with members:
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "three practical aspects of massive mimo intermittent user activity pilot synchronism and asymmetric deployment"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "blind pilot decontamination"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-6 (induced by source paper "superimposed pilots are superior for mitigating pilot contamination in massive mimo part i theory and channel estimation"):
  with members:
    - "massive mimo ten myths and one critical question"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "blind pilot decontamination"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"

Connected through hyperedge HE-7 (induced by source paper "asymptotic optimality of massive mimo systems using densely spaced transmit antennas"):
  with members:
    - "blind pilot decontamination"
    - "large system analysis of correlated mimo multiple access channels with arbitrary signaling in the presence of interference"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of joint channel and data estimation for mimo ds cdma systems"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"
    - "asymptotic analysis of general multiuser detectors in mimo ds cdma channels"
    - "vector precoding for wireless mimo systems and its replica analysis"
    - "analysis of cdma systems that are characterized by eigenvalue spectrum"

Connected through hyperedge HE-8 (induced by source paper "joint channel and data estimation for large mimo systems with low precision adcs"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "performance limits of massive mimo systems based on bayes optimal inference"
    - "bilinear generalized approximate message passing part i derivation"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "massive mimo with 1 bit adc"
    - "distributed reception with spatial multiplexing mimo systems for the internet of things"

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)