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: "protograph based ldpc code design for shaped bit metric decoding"
  Abstract: A protograph-based low-density parity-check (LDPC) code design technique for bandwidth-efficient coded modulation is presented. The approach jointly optimizes the LDPC code node degrees and the mapping of the coded bits to the bit-interleaved coded modulation (BICM) bit-channels. For BICM with uniform input and for BICM with probabilistic shaping, binary-input symmetric-output surrogate channels for the code design are used. The constructed codes for uniform inputs perform as good as the multi-edge type codes of Zhang and Kschischang (2013). For 8-ASK and 64-ASK with probabilistic shaping, codes of rates 2/3 and 5/6 with blocklength 64800 are designed, which operate within 0.63dB and 0.69dB of continuous AWGN capacity for a target frame error rate of 1e-3 at spectral efficiencies of 1.38 and 4.25 bits/channel use, respectively.

Connected through hyperedge HE-1 (induced by source paper "protograph based ldpc code design for probabilistic shaping with on off keying"):
  with members:
    - "probabilistic eigenvalue shaping for nonlinear fourier transform transmission"
    - "constant composition distribution matching"

Connected through hyperedge HE-2 (induced by source paper "optimization of bit mapping and quantized decoding for off the shelf protograph ldpc codes with application to ieee 802 3ca"):
  with members:
    - "achievable rates for probabilistic shaping"
    - "constant composition distribution matching"
    - "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"
    - "improving soft fec performance for higher order modulations via optimized bit channel mappings"
    - "staircase codes fec for 100 gb s otn"

Connected through hyperedge HE-3 (induced by source paper "ultra sparse non binary ldpc codes for probabilistic amplitude shaping"):
  with members:
    - "probabilistic shaping and non binary codes"
    - "design and analysis of nonbinary ldpc codes for arbitrary discrete memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "efficient polar code construction for higher order modulation"):
  with members:
    - "bit permuted coded modulation for polar codes"
    - "a comparative study of polar code constructions for the awgn channel"
    - "list decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "design of robust protograph based ldpc codes for rate adaptation via probabilistic shaping"):
  with members:
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "constant composition distribution matching"
    - "achievable rates for shaped bit metric decoding"
    - "deterministic constructions for large girth protograph ldpc codes"

Connected through hyperedge HE-6 (induced by source paper "achievable rates for shaped bit metric decoding"):
  with members:
    - "protograph based ldpc code design for bit metric decoding"
    - "probabilistic signal shaping for bit metric decoding"
    - "theoretic shaping bounds for single letter constraints and mismatched decoding"
    - "an efficient algorithm to calculate bicm capacity"
    - "optimal alphabets and binary labelings for bicm at low snr"

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)