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: "finite lattice size effects in mimo detection"
  Abstract: Many powerful data detection algorithms employed in multiple-input multiple-output (MIMO) communication systems, such as sphere decoding (SD) and lattice-reduction (LR)-aided detection, were initially designed for infinite lattices. Detection in MIMO systems is, however, based on finite lattices. In this paper, we systematically study the consequences of finite lattice-size for the performance and complexity of MIMO detection algorithms formulated for infinite lattices. Specifically, we find, considering performance and complexity, that LR does not seem to offer advantages when used in conjunction with SD.

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 "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"):
  with members:
    - "algebraic reduction for space time codes based on quaternion algebras"
    - "on the densest mimo lattices from cyclic division algebras"
    - "on fast decodable space time block codes"
    - "lll reduction achieves the receive diversity in mimo decoding"
    - "diversity multiplexing tradeoff in selective fading mimo channels"
    - "approximately universal space time codes for the parallel multi block and cooperative dynamic decode and forward channels"
    - "on the limitations of the naive lattice decoding"
    - "towards the optimal amplify and forward cooperative diversity scheme"

Connected through hyperedge HE-3 (induced by source paper "soft input soft output single tree search sphere decoding"):
  with members:
    - "a scalable vlsi architecture for soft input soft output depth first sphere 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)