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: "optimized bit mappings for spatially coupled ldpc codes over parallel binary erasure channels"
  Abstract: In many practical communication systems, one binary encoder/decoder pair is used to communicate over a set of parallel channels. Examples of this setup include multi-carrier transmission, rate-compatible puncturing of turbo-like codes, and bit-interleaved coded modulation (BICM). A bit mapper is commonly employed to determine how the coded bits are allocated to the channels. In this paper, we study spatially coupled low-density parity check codes over parallel channels and optimize the bit mapper using BICM as the driving example. For simplicity, the parallel bit channels that arise in BICM are replaced by independent binary erasure channels (BECs). For two parallel BECs modeled according to a 4-PAM constellation labeled by the binary reflected Gray code, the optimization results show that the decoding threshold can be improved over a uniform random bit mapper, or, alternatively, the spatial chain length of the code can be reduced for a given gap to capacity. It is also shown that for rate-loss free, circular (tail-biting) ensembles, a decoding wave effect can be initiated using only an optimized bit mapper.

Connected through hyperedge HE-1 (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"
    - "protograph based ldpc code design for shaped bit metric decoding"
    - "constant composition distribution matching"
    - "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-2 (induced by source paper "improving the decoding threshold of tailbiting spatially coupled ldpc codes by energy shaping"):
  with members:
    - "spatially coupled ldpc codes constructed from protographs"
    - "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"

Connected through hyperedge HE-3 (induced by source paper "coding for channels with snr variation spatial coupling and efficient interleaving"):
  with members:
    - "windowed decoding of spatially coupled codes"

Connected through hyperedge HE-4 (induced by source paper "wave like decoding of tail biting spatially coupled ldpc codes through iterative demapping"):
  with members:
    - "spatially coupled ldpc codes constructed from protographs"
    - "triggering wave like convergence of tail biting spatially coupled ldpc codes"
    - "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-5 (induced by source paper "triggering wave like convergence of tail biting spatially coupled ldpc codes"):
  with members:
    - "a generalization of threshold saturation application to spatially coupled bicm id"
    - "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "performance of spatially coupled ldpc codes and threshold saturation over bicm channels"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "threshold saturation on bms channels via spatial coupling"

Connected through hyperedge HE-6 (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"
    - "protograph based ldpc code design for bit metric decoding"
    - "constant composition distribution matching"
    - "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-7 (induced by source paper "protograph based ldpc code design for bit metric decoding"):
  with members:
    - "constant composition distribution matching"
    - "improving soft fec performance for higher order modulations via optimized bit channel mappings"
    - "achievable rates for shaped bit metric decoding"
    - "probabilistic signal shaping for bit metric decoding"

Connected through hyperedge HE-8 (induced by source paper "improving soft fec performance for higher order modulations via optimized bit channel mappings"):
  with members:
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "awgn channel analysis of terminated ldpc convolutional 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)