human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"):
  Abstract: This paper identifies the first general, explicit, and nonrandom MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statistics, for all channel dimensions, and most interestingly, irrespective of the particular lattice-code applied. As a special case, it is established that the LLL-based LR-aided linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal decoding of any lattice code at a worst-case complexity that grows at most linearly in the data rate. This represents a fundamental reduction in the decoding complexity when compared to ML decoding whose complexity is generally exponential in the rate. The results' generality lends them applicable to a plethora of pertinent communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI, cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality of the LR-aided linear decoder is guaranteed. The adopted approach yields insight, and motivates further study, into joint transceiver designs with an improved SNR gap to ML decoding.

Includes members:
  Member "finite lattice size effects in mimo detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization")
  Member "algebraic reduction for space time codes based on quaternion algebras":
    also connected through other hyperedges:
      (none)
  Member "on the densest mimo lattices from cyclic division algebras":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new design criterion for spherically shaped division algebra based space time codes")
  Member "on fast decodable space time block codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends")
  Member "lll reduction achieves the receive diversity in mimo decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")
  Member "diversity multiplexing tradeoff in selective fading mimo channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the streaming dmt of fading channels")
  Member "approximately universal space time codes for the parallel multi block and cooperative dynamic decode and forward channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "almost universal codes achieving ergodic mimo capacity within a constant gap")
  Member "on the limitations of the naive lattice decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization")

Question: Please tell me which class the source paper 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)