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: "fast sparse superposition codes have exponentially small error probability for r l c"
  Abstract: For the additive white Gaussian noise channel with average codeword power constraint, sparse superposition codes are developed. These codes are based on the statistical high-dimensional regression framework. The paper [IEEE Trans. Inform. Theory 55 (2012), 2541 - 2557] investigated decoding using the optimal maximum-likelihood decoding scheme. Here a fast decoding algorithm, called adaptive successive decoder, is developed. For any rate R less than the capacity C communication is shown to be reliable with exponentially small error probability.

Connected through hyperedge HE-1 (induced by source paper "construction of capacity achieving lattice codes polar lattices"):
  with members:
    - "lattice index coding"
    - "finite length scaling for polar codes"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "how to achieve the capacity of asymmetric channels"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "multilevel polar coded modulation"
    - "improved bounds on the finite length scaling of polar codes"
    - "achieving awgn channel capacity with lattice gaussian coding"

Connected through hyperedge HE-2 (induced by source paper "lossy compression via sparse linear regression computationally efficient encoding and decoding"):
  with members:
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "sparse regression codes for multi terminal source and channel coding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "the dispersion of lossy source coding"
    - "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"

Connected through hyperedge HE-3 (induced by source paper "the gaussian rate distortion function of sparse regression codes with optimal encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "chasing the k colorability threshold"
    - "the dispersion of lossy source coding"

Connected through hyperedge HE-4 (induced by source paper "the rate distortion function and error exponent of sparse regression codes with optimal encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "chasing the k colorability threshold"

Connected through hyperedge HE-5 (induced by source paper "sparse regression codes for multi terminal source and channel coding"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

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)