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: "ultra sparse non binary ldpc codes for probabilistic amplitude shaping"
  Abstract: This work shows how non-binary low-density parity-check codes over GF($2^p$) can be combined with probabilistic amplitude shaping (PAS) (B\"ocherer, et al., 2015), which combines forward-error correction with non-uniform signaling for power-efficient communication. Ultra-sparse low-density parity-check codes over GF(64) and GF(256) gain 0.6 dB in power efficiency over state-of-the-art binary LDPC codes at a spectral efficiency of 1.5 bits per channel use and a blocklength of 576 bits. The simulation results are compared to finite length coding bounds and complemented by density evolution analysis.

Connected through hyperedge HE-1 (induced by source paper "efficient error correcting codes in the short blocklength regime"):
  with members:
    - "low complexity joint channel estimation and list decoding of short codes"
    - "low latency short packet transmissions fixed length or harq"
    - "design of polar codes in 5g new radio"
    - "joint design of convolutional code and crc under serial list viterbi decoding"
    - "on the error probability of short concatenated polar and cyclic codes with interleaving"
    - "short packets over block memoryless fading channels pilot assisted or noncoherent transmission"
    - "polar code construction for list decoding"
    - "beta expansion a theoretical framework for fast and recursive construction of polar codes"

Connected through hyperedge HE-2 (induced by source paper "probabilistically shaped multi level coding with polar codes for fading channels"):
  with members:
    - "polar codes with integrated probabilistic shaping for 5g new radio"
    - "achievable rates for probabilistic shaping"
    - "how to achieve the capacity of asymmetric channels"
    - "list decoding of polar codes"

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)