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: "construction of capacity achieving lattice codes polar lattices"
  Abstract: In this paper, we propose a new class of lattices constructed from polar codes, namely polar lattices, to achieve the capacity $\frac{1}{2}\log(1+\text{SNR})$ of the additive white Gaussian-noise (AWGN) channel for any signal-to-noise ratio (SNR). Our construction follows the multilevel approach of Forney et al., where on each level we construct a capacity-achieving polar code. The component polar codes are naturally nested, thereby fulfilling the requirement of the multilevel lattice construction. We prove that polar lattices are AWGN-good, in the sense that the error probability (for infinite lattice decoding) vanishes for any fixed volume-to-noise ratio (VNR) greater than $2\pi e$. Furthermore, using the technique of source polarization, we propose discrete Gaussian shaping over the polar lattice to satisfy the power constraint. The proposed polar lattices permit low-complexity multistage successive cancellation decoding. Both the construction and shaping are explicit, and the overall complexity of encoding and decoding is $O(N\log N)$ for any fixed target error probability.

Connected through hyperedge HE-1 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "neural lattice decoders"):
  with members:
    - "a neural network lattice decoding algorithm"
    - "leech constellations of construction a lattices"
    - "learning to decode linear codes using deep learning"
    - "low density lattice codes"

Connected through hyperedge HE-3 (induced by source paper "capacity achieving sparse superposition codes via approximate message passing decoding"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "replica analysis and approximate message passing decoder for superposition codes"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"

Connected through hyperedge HE-4 (induced by source paper "awgn goodness is enough capacity achieving lattice codes based on dithered probabilistic shaping"):
  with members:
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "algebraic lattice codes achieve the capacity of the compound block fading channel"
    - "lda lattices without dithering achieve capacity on the gaussian channel"
    - "almost universal codes for fading wiretap channels"
    - "on the lattice smoothing parameter problem"
    - "semantically secure lattice codes for the gaussian wiretap channel"

Connected through hyperedge HE-5 (induced by source paper "multilevel ldpc lattices with efficient encoding and decoding and a generalization of construction d"):
  with members:
    - "ldpc lattice codes for full duplex relay channels"
    - "lda lattices without dithering achieve capacity on the gaussian channel"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "connections between construction d and related constructions of lattices"
    - "on the performance of 1 level ldpc lattices"
    - "strong secrecy on the binary erasure wiretap channel using large girth ldpc codes"
    - "integer forcing linear receivers"

Connected through hyperedge HE-6 (induced by source paper "polar codes and polar lattices for the heegard berger problem"):
  with members:
    - "polar codes for distributed hierarchical source coding"
    - "polar lattices for lossy compression"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "polar codes are optimal for lossy source coding"

Connected through hyperedge HE-7 (induced by source paper "polar codes and polar lattices for independent fading channels"):
  with members:
    - "algebraic lattice codes achieve the capacity of the compound block fading channel"
    - "polar codes for broadcast channels"
    - "almost universal codes achieving ergodic mimo capacity within a constant gap"
    - "number field lattices achieve gaussian and rayleigh channel capacity within a constant gap"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "how to achieve the capacity of asymmetric channels"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "polar coding for fading channels binary and exponential channel cases"

Connected through hyperedge HE-8 (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"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "on the performance of 1 level ldpc lattices"
    - "finite dimensional infinite constellations"
    - "turbo lattices construction and error decoding performance"

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)