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: "how to find good finite length codes from art towards science"
  Abstract: We explain how to optimize finite-length LDPC codes for transmission over the binary erasure channel. Our approach relies on an analytic approximation of the erasure probability. This is in turn based on a finite-length scaling result to model large scale erasures and a union bound involving minimal stopping sets to take into account small error events. We show that the performances of optimized ensembles as observed in simulations are well described by our approximation. Although we only address the case of transmission over the binary erasure channel, our method should be applicable to a more general setting.

Connected through hyperedge HE-1 (induced by source paper "effects of single cycle structure on iterative decoding of low density parity check codes"):
  with members:
    - "finite length analysis of irregular expurgated ldpc codes under finite number of iterations"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "subsampling at information theoretically optimal rates"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "optimal rate and maximum erasure probability ldpc codes in binary erasure channel"):
  with members:
    - "on universal properties of capacity approaching ldpc code ensembles"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-4 (induced by source paper "analytical solution of covariance evolution for irregular ldpc codes"):
  with members:
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-5 (induced by source paper "optimal rate for irregular ldpc codes in binary erasure channel"):
  with members:
    - "on universal properties of capacity approaching ldpc code ensembles"
    - "channel coding the road to channel capacity"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-6 (induced by source paper "a sequential algorithm for generating random graphs"):
  with members:
    - "generating random graphs with large girth"

Connected through hyperedge HE-7 (induced by source paper "on universal properties of capacity approaching ldpc code ensembles"):
  with members:
    - "on the design of universal ldpc codes"
    - "modern coding theory the statistical mechanics and computer science point of view"
    - "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity"
    - "channel coding the road to channel capacity"
    - "the generalized area theorem and some of its consequences"
    - "capacity achieving codes with bounded graphical complexity on noisy channels"
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-8 (induced by source paper "generating random graphs with large girth"):
  with members:
    - "a sequential algorithm for generating random graphs"

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)