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: "lr aided mmse lattice decoding is dmt optimal for all approximately universal codes"
  Abstract: Currently for the nt x nr MIMO channel, any explicitly constructed space-time (ST) designs that achieve optimality with respect to the diversity multiplexing tradeoff (DMT) are known to do so only when decoded using maximum likelihood (ML) decoding, which may incur prohibitive decoding complexity. In this paper we prove that MMSE regularized lattice decoding, as well as the computationally efficient lattice reduction (LR) aided MMSE decoder, allows for efficient and DMT optimal decoding of any approximately universal lattice-based code. The result identifies for the first time an explicitly constructed encoder and a computationally efficient decoder that achieve DMT optimality for all multiplexing gains and all channel dimensions. The results hold irrespective of the fading statistics.

Connected through hyperedge HE-1 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling lattice reduction and decoding optimization"):
  with members:
    - "boosted kz and lll algorithms"
    - "polar codes and polar lattices for independent fading channels"
    - "scan order in gibbs sampling models in which it matters and bounds on how much"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"
    - "markov chain monte carlo algorithms for lattice gaussian sampling"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "decoding by embedding correct decoding radius and dmt optimality"

Connected through hyperedge HE-2 (induced by source paper "performance complexity analysis for mac ml based decoding with user selection"):
  with members:
    - "achieving a vanishing snr gap to exact lattice decoding at a subexponential complexity"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "minimizing the complexity of fast sphere decoding of stbcs"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "approximately universal optimality over several dynamic and non dynamic cooperative diversity schemes for wireless networks"

Connected through hyperedge HE-3 (induced by source paper "decoding by sampling a randomized lattice algorithm for bounded distance decoding"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "augmented lattice reduction for mimo decoding"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "improved analysis of kannan s shortest lattice vector algorithm"
    - "on fast decodable space time block codes"
    - "lll reduction achieves the receive diversity in mimo decoding"

Connected through hyperedge HE-4 (induced by source paper "on the proximity factors of lattice reduction aided decoding"):
  with members:
    - "lll reduction achieves the receive diversity in mimo decoding"
    - "on the limitations of the naive lattice decoding"
    - "towards the optimal amplify and forward cooperative diversity scheme"

Connected through hyperedge HE-5 (induced by source paper "fundamental rate reliability complexity limits in outage limited mimo communications"):
  with members:
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "space time codes from structured lattices"
    - "on fast decodable space time block codes"
    - "d mg tradeoff and optimal codes for a class of af and df cooperative communication protocols"
    - "optimal space time codes for the mimo amplify and forward cooperative channel"

Connected through hyperedge HE-6 (induced by source paper "variants of the lll algorithm in digital communications complexity analysis and fixed complexity implementation"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "augmented lattice reduction for mimo decoding"
    - "randomized lattice decoding"
    - "lll reduction achieves the receive diversity in mimo decoding"

Connected through hyperedge HE-7 (induced by source paper "randomized lattice decoding"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "on fast decodable space time block codes"
    - "lll reduction achieves the receive diversity in mimo 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)