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: "increasing achievable information rates via geometric shaping"
  Abstract: Achievable information rates are used as a metric to design novel modulation formats via geometric shaping. The proposed geometrically shaped 256-ary constellation achieves SNR gains of up to 1.18 dB.

Connected through hyperedge HE-1 (induced by source paper "on product codes with probabilistic amplitude shaping for high throughput fiber optic systems"):
  with members:
    - "binary message passing decoding of product codes based on generalized minimum distance decoding"
    - "binary message passing decoding of product like codes"
    - "iterative bounded distance decoding of product codes with scaled reliability"
    - "on low complexity decoding of product codes for high throughput fiber optic systems"
    - "energy efficient soft assisted product decoders"
    - "achievable rates for probabilistic shaping"
    - "miscorrection free decoding of staircase codes"
    - "probabilistic amplitude shaping with hard decision decoding and staircase codes"

Connected through hyperedge HE-2 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths"):
  with members:
    - "exploring and experimenting with shaping designs for next generation optical communications"
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "experimental verification of rate flexibility and probabilistic shaping by 4d signaling"
    - "experimental comparison of probabilistic shaping methods for unrepeated fiber transmission"
    - "a survey on fiber nonlinearity compensation for 400 gb s and beyond optical communication systems"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"

Connected through hyperedge HE-3 (induced by source paper "end to end learning for gmi optimized geometric constellation shape"):
  with members:
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"
    - "constant composition distribution matching"

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)