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: "augmented lattice reduction for mimo decoding"
  Abstract: Lattice reduction algorithms, such as the LLL algorithm, have been proposed as preprocessing tools in order to enhance the performance of suboptimal receivers in MIMO communications. In this paper we introduce a new kind of lattice reduction-aided decoding technique, called augmented lattice reduction, which recovers the transmitted vector directly from the change of basis matrix, and therefore doesn't entail the computation of the pseudo-inverse of the channel matrix or its QR decomposition. We prove that augmented lattice reduction attains the maximum receive diversity order of the channel; simulation results evidence that it significantly outperforms LLL-SIC detection without entailing any additional complexity. A theoretical bound on the complexity is also derived.

Connected through hyperedge HE-1 (induced by source paper "decoding by sampling part ii derandomization and soft output decoding"):
  with members:
    - "decoding by embedding correct decoding radius and dmt optimality"
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "decoding by embedding correct decoding radius and dmt optimality"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "optimal space time codes for the mimo amplify and forward cooperative channel"

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"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "lr aided mmse lattice decoding is dmt optimal for all approximately universal codes"
    - "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 "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"
    - "randomized lattice decoding"
    - "lr aided mmse lattice decoding is dmt optimal for all approximately universal codes"
    - "lll reduction achieves the receive diversity in mimo decoding"

Connected through hyperedge HE-5 (induced by source paper "constellation precoded multiple beamforming"):
  with members:
    - "constellation precoded beamforming"
    - "diversity analysis of bit interleaved coded multiple beamforming"
    - "reduced complexity sphere decoding for square qam via a new lattice representation"
    - "a near maximum likelihood decoding algorithm for mimo systems based on semi definite programming"

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)