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 dimensional infinite constellations"
  Abstract: In the setting of a Gaussian channel without power constraints, proposed by Poltyrev in 1994, the codewords are points in an n-dimensional Euclidean space (an infinite constellation) and the tradeoff between their density and the error probability is considered. The normalized log density (NLD) plays the role of the communication rate, and capacity as well as error exponent bounds for this setting are known. This paper considers the infinite constellation setting in the finite block-length (dimension) regime. A simplified expression for Poltyrev's achievability bound is found and it is shown to be closely related to the sphere converse bound and to a recently proposed achievability bound based on point processes. The bounds are then analyzed asymptotically for growing n: for fixed NLD, the bounds turn out to be extremely tight compared to previous error exponent analysis. For fixed error probability e, it is shown that the gap of the highest achievable NLD to the optimal NLD (Poltyrev's capacity) is approximately √{[1/(2n)]}Q-1(e) , where Q is the standard complementary Gaussian cumulative distribution function, thus extending the channel dispersion analysis to infinite constellations. Connections to the error exponent of the power-constrained Gaussian channel and to the volume-to-noise ratio as a figure of merit are discussed. Finally, the new tight bounds are compared to state-of-the-art coding schemes.

Connected through hyperedge HE-1 (induced by source paper "on the decoding of barnes wall lattices"):
  with members:
    - "multilevel constructions coding packing and geometric uniformity"
    - "soft decision decoding of reed muller codes recursive lists"
    - "turbo lattices construction and error decoding performance"
    - "practical encoders and decoders for euclidean codes from barnes wall lattices"
    - "list decoding barnes wall lattices"
    - "low density lattice codes"

Connected through hyperedge HE-2 (induced by source paper "leech constellations of construction a lattices"):
  with members:
    - "some goodness properties of lda lattices"
    - "encoding and indexing of lattice codes"
    - "lda lattices without dithering achieve capacity on the gaussian channel"
    - "low dimensional shaping for high dimensional lattice codes"
    - "construction of capacity achieving lattice codes polar lattices"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "on the performance of 1 level ldpc lattices"
    - "turbo lattices construction and error decoding performance"

Connected through hyperedge HE-3 (induced by source paper "asymptotic estimates in information theory with non vanishing error probabilities"):
  with members:
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "the third order term in the normal approximation for the awgn channel"
    - "strong converse and second order asymptotics of channel resolvability"
    - "channels with cost constraints strong converse and dispersion"
    - "secret key agreement general capacity and second order asymptotics"
    - "second order coding rates for channels with state"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

Connected through hyperedge HE-4 (induced by source paper "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn"):
  with members:
    - "the third order term in the normal approximation for the awgn channel"

Connected through hyperedge HE-5 (induced by source paper "on dispersions of discrete memoryless channels with noncausal state information at the encoder"):
  with members:
    - "on the dispersions of three network information theory problems"
    - "the dispersion of lossy source coding"
    - "the dispersion of joint source channel coding"
    - "the gelfand pinsker channel strong converse and upper bound for the reliability function"
    - "information spectrum approach to second order coding rate in channel coding"
    - "capacity and random coding exponents for channel coding with side information"

Connected through hyperedge HE-6 (induced by source paper "a tight upper bound for the third order asymptotics for most discrete memoryless channels"):
  with members:
    - "information spectrum approach to second order coding rate in channel coding"
    - "simple channel coding bounds"

Connected through hyperedge HE-7 (induced by source paper "the random coding bound is tight for the average linear code or lattice"):
  with members:
    - "a simple proof for the existence of good pairs of nested lattices"
    - "on the asymptotic accuracy of the union bound"

Connected through hyperedge HE-8 (induced by source paper "moderate deviations in channel coding"):
  with members:
    - "on the dispersions of three network information theory problems"
    - "refinement of the random coding bound"
    - "moderate deviations analysis of binary hypothesis testing"
    - "refinement of the sphere packing bound asymmetric channels"
    - "an information theoretic characterization of channels that die"
    - "fixed length lossy compression in the finite blocklength regime"
    - "on refined versions of the azuma hoeffding inequality with applications in information theory"
    - "the dispersion of lossy source coding"

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)