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 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 are constructed and used for code design. The constructed codes perform as good as multi-edge type codes of Zhang and Kschischang (2013). For 64-ASK with probabilistic shaping, a blocklength 64800 code is constructed that operates within 0.69 dB of 0.5log(1+SNR) at a spectral efficiency of 4.2 bits/channel use and a frame error rate of 1e-3.

Connected through hyperedge HE-1 (induced by source paper "bandwidth efficient and rate matched low density parity check coded modulation"):
  with members:
    - "arithmetic distribution matching"
    - "how to achieve the capacity of asymmetric channels"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "optimal non uniform mapping for probabilistic shaping"
    - "operating ldpc codes with zero shaping gap"
    - "constellation shaping for bit interleaved ldpc coded apsk"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-2 (induced by source paper "protograph based ldpc code design for shaped bit metric decoding"):
  with members:
    - "triggering wave like convergence of tail biting spatially coupled ldpc codes single and dual channel setup"
    - "constant composition distribution matching"
    - "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"
    - "achievable rates for shaped bit metric decoding"
    - "probabilistic signal shaping for bit metric decoding"
    - "deterministic constructions for large girth protograph ldpc codes"

Connected through hyperedge HE-3 (induced by source paper "achievable rates for shaped bit metric decoding"):
  with members:
    - "protograph based ldpc code design for shaped 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)