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: "lattice sequential decoder for coded mimo channel performance and complexity analysis"
  Abstract: In this paper, the performance limit of lattice sequential decoder for lattice space-time coded MIMO channel is analysed. We determine the rates achievable by lattice coding and sequential decoding applied to such channel. The diversity-multiplexing tradeoff (DMT) under lattice sequential decoding is derived as a function of its parameter---the bias term. The bias parameter is critical for controlling the amount of computations required at the decoding stage. Achieving low decoding complexity requires increasing the value of the bias term. However, this is done at the expense of losing the optimal tradeoff of the channel. We show how such a decoder can bridge the gap between lattice decoder and low complexity decoders. Moreover, the computational complexity of lattice sequential decoder is analysed. Specifically, we derive the tail distribution of the decoder's computational complexity in the high signal-to-noise ratio regime. Similar to the conventional sequential decoder used in discrete memoryless channel, our analysis reveals that the tail distribution of such low complexity decoder is of a Pareto-type. Also, the tail exponent of the complexity distribution is shown to be equivalent to the DMT achieved by such coding and decoding schemes. We show analytically how minimum-mean square-error decision feed-back equalization can significantly improve the tail exponent and as a consequence reduce computational complexity. An interesting result shows that there exists a cut-off multiplexing gain for which the average computational complexity of the decoder remains bounded as long as we operate below such value.

Connected through hyperedge HE-1 (induced by source paper "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"):
  with members:
    - "efficient soft input soft output tree detection via an improved path metric"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "faster recursions in sphere decoding"
    - "lattice sequential decoding for last coded mimo channels achievable rate dmt and complexity analysis"
    - "mimo detection for high order qam based on a gaussian tree approximation"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "soft input soft output single tree search sphere decoding"
    - "near optimal detection in mimo systems using gibbs sampling"

Connected through hyperedge HE-2 (induced by source paper "time out lattice sequential decoding for the mimo arq channel"):
  with members:
    - "asymptotic performance of linear receivers in mimo fading channels"

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)