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: "a simple proof of threshold saturation for coupled scalar recursions"
  Abstract: Convolutional low-density parity-check (LDPC) codes (or spatially-coupled codes) have now been shown to achieve capacity on binary-input memoryless symmetric channels. The principle behind this surprising result is the threshold-saturation phenomenon, which is defined by the belief-propagation threshold of the spatially-coupled ensemble saturating to a fundamental threshold defined by the uncoupled system. #R##N#Previously, the authors demonstrated that potential functions can be used to provide a simple proof of threshold saturation for coupled scalar recursions. In this paper, we present a simple proof of threshold saturation that applies to a wide class of coupled vector recursions. The conditions of the theorem are verified for the density-evolution equations of: (i) joint decoding of irregular LDPC codes for a Slepian-Wolf problem with erasures, (ii) joint decoding of irregular LDPC codes on an erasure multiple-access channel, and (iii) general protograph codes on the BEC. This proves threshold saturation for these systems.

Connected through hyperedge HE-1 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "fast scalable construction of minimal perfect hash functions"
    - "bubing massive crawling for the masses"
    - "the multiple orientability thresholds for random hypergraphs"
    - "on the insertion time of random walk cuckoo hashing"
    - "high speed hashing for integers and strings"
    - "cache oblivious peeling of random hypergraphs"
    - "thresholds of spatially coupled systems via lyapunov s method"

Connected through hyperedge HE-2 (induced by source paper "design of irregular sc ldpc codes with non uniform degree distributions by linear programing"):
  with members:
    - "on the design of fast convergent ldpc codes for the bec an optimization approach"
    - "spatially coupled ldpc codes constructed from protographs"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "on the convergence speed of spatially coupled ldpc ensembles"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "spatially coupled repeat accumulate codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-3 (induced by source paper "avoiding burst like error patterns in windowed decoding of spatially coupled ldpc codes"):
  with members:
    - "combating error propagation in window decoding of braided convolutional codes"
    - "non uniformly coupled ldpc codes better thresholds smaller rate loss and less complexity"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "windowed decoding of spatially coupled codes"

Connected through hyperedge HE-4 (induced by source paper "spatially coupled turbo like codes a new trade off between waterfall and error floor"):
  with members:
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-5 (induced by source paper "universal sparse superposition codes with spatial coupling and gamp decoding"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "a compressed sensing approach for distribution matching"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "state evolution for approximate message passing with non separable functions"
    - "generalized approximate message passing decoder for universal sparse superposition codes"

Connected through hyperedge HE-6 (induced by source paper "mutual information and optimality of approximate message passing in random linear estimation"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "the mutual information in random linear estimation"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"

Connected through hyperedge HE-7 (induced by source paper "i mmse relations in random linear estimation and a sub extensive interpolation method"):
  with members:
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "support recovery with sparsely sampled free random matrices"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-8 (induced by source paper "spatially coupled turbo like codes"):
  with members:
    - "finite length weight enumerator analysis of braided convolutional codes"
    - "spatially coupled ldpc codes constructed from protographs"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "spatially coupled turbo codes principles and finite length performance"
    - "braided convolutional codes a class of spatially coupled turbo like codes"
    - "analysis and design of tuned turbo codes"
    - "staircase codes fec for 100 gb s otn"
    - "approaching capacity at high rates with iterative hard decision decoding"

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)