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: "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
  Abstract: In this paper, we deal with time-invariant spatially coupled low-density parity-check convolutional codes (SC-LDPC-CCs). Classic design approaches usually start from quasi-cyclic low-density parity-check block codes and exploit suitable unwrapping procedures to obtain SC-LDPC-CCs. We show that the direct design of the SC-LDPC-CCs syndrome former matrix or, equivalently, the symbolic parity-check matrix, leads to codes with smaller syndrome former constraint lengths with respect to the best solutions available in the literature. We provide theoretical lower bounds on the syndrome former constraint length for the most relevant families of SC-LDPC-CCs, under constraints on the minimum length of cycles in their Tanner graphs. We also propose new code design techniques that approach or achieve such theoretical limits.

Connected through hyperedge HE-1 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "improving the decoding threshold of tailbiting spatially coupled ldpc codes by energy shaping"
    - "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"
    - "wave like decoding of tail biting spatially coupled ldpc codes through iterative demapping"
    - "finite length analysis of spatially coupled regular ldpc ensembles on burst erasure channels"
    - "efficient multi dimensional mapping using qam constellations for bicm id"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"

Connected through hyperedge HE-2 (induced by source paper "integer ring sieve irs for constructing compact qc ldpc codes with large girth"):
  with members:
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"
    - "the cycle consistency matrix approach to absorbing sets in separable circulant based ldpc codes"
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "lowering the error floor of ldpc codes using cyclic liftings"

Connected through hyperedge HE-3 (induced by source paper "efficient search and elimination of harmful objects in optimized qc sc ldpc codes"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "a generalized algebraic approach to optimizing sc ldpc codes"
    - "spatially coupled ldpc codes constructed from protographs"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-4 (induced by source paper "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"):
  with members:
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "lower bounds on the lifting degree of single edge and multiple edge qc ldpc codes by difference matrices"
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

Connected through hyperedge HE-5 (induced by source paper "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"):
  with members:
    - "lower bounds on the lifting degree of single edge and multiple edge qc ldpc codes by difference matrices"
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "terminated ldpc convolutional codes with thresholds close to capacity"

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)