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: "information rates and error exponents for probabilistic amplitude shaping"
  Abstract: Probabilistic Amplitude Shaping (PAS) is a coded-modulation scheme in which the encoder is a concatenation of a distribution matcher with a systematic Forward Error Correction (FEC) code. For reduced computational complexity the decoder can be chosen as a concatenation of a mismatched FEC decoder and dematcher. This work studies the theoretic limits of PAS. The classical joint source-channel coding (JSCC) setup is modified to include systematic FEC and the mismatched FEC decoder. At each step error exponents and achievable rates for the corresponding setup are derived.

Connected through hyperedge HE-1 (induced by source paper "achievable information rates for probabilistic amplitude shaping a minimum randomness approach via random sign coding arguments"):
  with members:
    - "achievable rates for probabilistic shaping"
    - "achievable rates for shaped bit metric decoding"
    - "probabilistic signal shaping for bit metric decoding"

Connected through hyperedge HE-2 (induced by source paper "one and two bit message passing for sc ldpc codes with higher order modulation"):
  with members:
    - "a novel soft aided bit marking decoder for product codes"
    - "binary message passing decoding of product like codes"
    - "protograph based ldpc code design for ternary message passing decoding"
    - "energy efficient soft assisted product decoders"
    - "constant composition distribution matching"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "staircase codes fec for 100 gb s otn"
    - "analysis and design of binary message passing decoders"

Connected through hyperedge HE-3 (induced by source paper "probabilistic parity shaping for linear codes"):
  with members:
    - "protograph based ldpc code design for probabilistic shaping with on off keying"

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)