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 "decoding by embedding correct decoding radius and dmt optimality"):
  Abstract: The closest vector problem (CVP) and shortest (nonzero) vector problem (SVP) are the core algorithmic problems on Euclidean lattices. They are central to the applications of lattices in many problems of communications and cryptography. Kannan's embedding technique is a powerful technique for solving the approximate CVP; yet, its remarkable practical performance is not well understood. In this paper, the embedding technique is analyzed from a bounded distance decoding (BDD) viewpoint. We present two complementary analyses of the embedding technique: we establish a reduction from BDD to Hermite SVP (via unique SVP), which can be used along with any Hermite SVP solver (including, among others, the Lenstra, Lenstra and Lovasz (LLL) algorithm), and show that, in the special case of LLL, it performs at least as well as Babai's nearest plane algorithm (LLL-aided successive interference cancellation). The former analysis helps us to explain the folklore practical observation that unique SVP is easier than standard approximate SVP. It is proven that when the LLL algorithm is employed, the embedding technique can solve the CVP provided that the noise norm is smaller than a decoding radius λ1/(2γ) , where λ1 is the minimum distance of the lattice, and γ ≈ O(2n/4). This substantially improves the previously best known correct decoding bound γ ≈ O(2n) . Focusing on the applications of BDD to decoding of multiple-input multiple-output systems, we also prove that BDD of the regularized lattice is optimal in terms of the diversity-multiplexing gain tradeoff, and propose practical variants of embedding decoding which require no knowledge of the minimum distance of the lattice and/or further improve the error performance.

Includes members:
  Member "on the proximity factors of lattice reduction aided decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")
  Member "decoding by sampling a randomized lattice algorithm for bounded distance decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on low complexity lattice reduction algorithms for large scale mimo detection the blessing of sequential reduction")
  Member "augmented lattice reduction for mimo decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decoding by sampling part ii derandomization and soft output decoding")
  Member "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "optimal space time codes for the mimo amplify and forward cooperative channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unmanned aerial vehicle assisted cellular communication")

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)