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: "an improved lr aided k best algorithm for mimo detection"
  Abstract: Recently, lattice reduction (LR) technique has caught great attention for multi-input multi-output (MIMO) receiver because of its low complexity and high performance. However, when the number of antennas is large, LR-aided linear detectors and successive interference cancellation (SIC) detectors still exhibit considerable performance gap to the optimal maximum likelihood detector (MLD). To enhance the performance of the LR-aided detectors, the LR-aided K-best algorithm was developed at the cost of the extra complexity on the order $\mathcal{O}(N_t^2 K + N_t K^2)$, where $N_t$ is the number of transmit antennas and $K$ is the number of candidates. In this paper, we develop an LR-aided K-best algorithm with lower complexity by exploiting a priority queue. With the aid of the priority queue, our analysis shows that the complexity of the LR-aided K-best algorithm can be further reduced to $\mathcal{O}(N_t^2 K + N_t K {\rm log}_2(K))$. The low complexity of the proposed LR-aided K-best algorithm allows us to perform the algorithm for large MIMO systems (e.g., 50x50 MIMO systems) with large candidate sizes. Simulations show that as the number of antennas increases, the error performance approaches that of AWGN channel.

Connected through hyperedge HE-1 (induced by source paper "hardware architecture of complex k best mimo decoder"):
  with members:
    - "fixed point realization of iterative lr aided soft mimo decoding algorithm"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "on the limitations of the naive lattice decoding"

Connected through hyperedge HE-2 (induced by source paper "fixed point realization of iterative lr aided soft mimo decoding algorithm"):
  with members:
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "on the limitations of the naive lattice decoding"

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)