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 mimo detection a low complexity detector at high spectral efficiencies"
  Abstract: We consider large MIMO systems, where by 'large' we mean number of transmit and receive antennas of the order of tens to hundreds. Such large MIMO systems will be of immense interest because of the very high spectral efficienc ies possible in such systems. We present a low-complexity detector which achieves uncoded near-exponential diversity performance for hundreds of antennas (i.e., achieves near SISO AWGN performance in a large MIMO fading environment) with an average per-bit complexity of just O(NtNr), where Nt and Nr denote the number of transmit and receive antennas, respectively. With an outer turbo code, the proposed detector achieves good coded bit error performance as well. For example, in a 600 transmit and 600 receive antennas V-BLAST system with a high spectral efficiency of 200 bps/Hz (using BPSK and rate- 1/3 turbo code), our simulation results show that the proposed detector performs close to within about 4.6 dB from theoretical capacity. We also adopt the proposed detector for the low- complexity decoding of high-rate non-orthogonal space-time block codes (STBC) from division algebras (DA). For example, we have decoded the 16 × 16 full-rate non-orthogonal STBC from DA using the proposed detector and show that it performs close to within about 5.5 dB of the capacity using 4-QAM and rate-3/4 turbo code at a spectral efficiency of 24 bps/Hz. The practical feasibility of the proposed high-performance low- complexity detector could potentially trigger wide interest in the implementation of large MIMO systems. We also illustrate the applicability of the proposed detector in the low-complexity detection of large multicarrier CDMA (MC-CDMA) systems. In large MC-CDMA systems with hundreds of users, the proposed detector is shown to achieve near single-user performance at an average per-bit complexity linear in number of users, which is quite appealing for its use in practical CDMA systems.

Connected through hyperedge HE-1 (induced by source paper "high rate space time coded large mimo systems low complexity detection and channel estimation"):
  with members:
    - "asymptotic analysis of the performance of las algorithm for large mimo detection"
    - "reduced complexity sphere decoding for square qam via a new lattice representation"
    - "performance analysis of iterative channel estimation and multiuser detection in multipath ds cdma channels"

Connected through hyperedge HE-2 (induced by source paper "asymptotic analysis of the performance of las algorithm for large mimo detection"):
  with members:
    - "belief propagation based decoding of large non orthogonal stbcs"
    - "low complexity near ml decoding of large non orthogonal stbcs using reactive tabu search"
    - "high rate space time coded large mimo systems low complexity detection and channel estimation"
    - "reduced complexity sphere decoding for square qam via a new lattice representation"
    - "belief propagation based multi user detection"

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)