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: "achievable rates of mimo systems with linear precoding and iterative lmmse detection"
  Abstract: We establish area theorems for iterative detection over coded linear systems (including multiple-input multipleoutput (MIMO) channels, inter-symbol-interference (ISI) channels, and orthogonal frequency-division multiplexing (OFDM) systems). We propose a linear precoding technique that asymptotically ensures the Gaussianness of the messages passed in iterative detection, as the transmission block length tends to infinity. We show that the proposed linear precoding scheme with iterative linear minimum mean-square error (LMMSE) detection is potentially information lossless, under various assumptions on the availability of channel state information at the transmitter (CSIT). Numerical results are provided to verify our analysis.

Connected through hyperedge HE-1 (induced by source paper "capacity optimality of amp in coded systems"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "a unified framework of state evolution for message passing algorithms"
    - "optimal data detection in large mimo"
    - "near gaussian multiple access channel capacity detection and decoding"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "capacity achieving mimo noma iterative lmmse detection"):
  with members:
    - "near gaussian multiple access channel capacity detection and decoding"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "hybrid approximate message passing"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "hybrid approximate message passing"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-3 (induced by source paper "achievable rate region for iterative multi user detection via low cost gaussian approximation"):
  with members:
    - "near gaussian multiple access channel capacity detection and decoding"
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-4 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends"):
  with members:
    - "asymptotic performance analysis of a k hop amplify and forward relay mimo channel"
    - "iterative distributed minimum total mse approach for secure communications in mimo interference channels"
    - "asymptotic analysis of su mimo channels with transmitter noise and mismatched joint decoding"
    - "secure massive mimo transmission with an active eavesdropper"
    - "interference aligned space time transmission with diversity for the 2 times 2 x network"
    - "space time encoded miso broadcast channel with outdated csit an error rate and diversity performance analysis"
    - "high snr asymptotics of mutual information for discrete constellations with applications to bicm"
    - "differential distributed space time coding with imperfect synchronization in frequency selective channels"

Connected through hyperedge HE-5 (induced by source paper "capacity achieving iterative lmmse detection for mimo noma systems"):
  with members:
    - "the application of mimo to non orthogonal multiple access"
    - "a low complexity gaussian message passing iterative detector for massive mu mimo systems"
    - "uplink non orthogonal multiple access for 5g wireless networks"
    - "a semiblind two way training method for discriminatory channel estimation in mimo systems"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-6 (induced by source paper "gaussian message passing iterative detection for mimo noma systems with massive access"):
  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"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "belief propagation based multi user detection"

Connected through hyperedge HE-7 (induced by source paper "turbo compressed sensing with partial dft sensing matrix"):
  with members:
    - "analysis of compressed sensing with spatially coupled orthogonal matrices"
    - "support recovery with sparsely sampled free random matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

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)