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: "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
  Abstract: There is a fundamental relationship between belief propagation and maximum a posteriori decoding. A decoding algorithm, which we call the Maxwell decoder, is introduced and provides a constructive description of this relationship. Both, the algorithm itself and the analysis of the new decoder are reminiscent of the Maxwell construction in thermodynamics. This paper investigates in detail the case of transmission over the binary erasure channel, while the extension to general binary memoryless channels is discussed in a companion paper.

Connected through hyperedge HE-1 (induced by source paper "reed muller codes theory and algorithms"):
  with members:
    - "reed muller codes polarize"
    - "recursive projection aggregation decoding of reed muller codes"
    - "general strong polarization"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "decoding reed muller codes using minimum weight parity checks"
    - "on the bias of reed muller codes over odd prime fields"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "soft decision decoding of reed muller codes recursive lists"

Connected through hyperedge HE-2 (induced by source paper "spatially coupled generalized ldpc codes asymptotic analysis and finite length scaling"):
  with members:
    - "a refined scaling law for spatially coupled ldpc codes over the binary erasure channel"
    - "spatially coupled ldpc codes constructed from protographs"
    - "design of ldpc code ensembles with fast convergence properties"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "on the minimum distance of generalized spatially coupled ldpc codes"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "information theoretic thresholds from the cavity method"):
  with members:
    - "planting colourings silently"
    - "mutual information in rank one matrix estimation"
    - "the condensation phase transition in random graph coloring"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "conditioning and covariance on caterpillars"
    - "chasing the k colorability threshold"
    - "on the complexity of random satisfiability problems with planted solutions"

Connected through hyperedge HE-4 (induced by source paper "inactivation decoding of lt and raptor codes analysis and code design"):
  with members:
    - "bounds on the error probability of raptor codes"
    - "distance spectrum of fixed rate raptor codes with linear random precoders"
    - "inactivation decoding analysis for lt codes"
    - "lt code design for inactivation decoding"
    - "distributed raptor coding for erasure channels partially and fully coded cooperation"
    - "analysis of the second moment of the lt decoder"
    - "new model for rigorous analysis of lt codes"

Connected through hyperedge HE-5 (induced by source paper "a probabilistic peeling decoder to efficiently analyze generalized ldpc codes over the bec"):
  with members:
    - "design of ldpc code ensembles with fast convergence properties"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "on the minimum distance of generalized spatially coupled ldpc codes"
    - "spectral shape of doubly generalized ldpc codes efficient and exact evaluation"
    - "approaching capacity at high rates with iterative hard decision decoding"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-6 (induced by source paper "ml and near ml decoding of ldpc codes over the bec bounds and decoding algorithms"):
  with members:
    - "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance"
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "decoding of convolutional codes over the erasure channel"
    - "analysis of quasi cyclic ldpc codes under ml decoding over the erasure channel"

Connected through hyperedge HE-7 (induced by source paper "time invariant ldpc convolutional codes"):
  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"
    - "deriving good ldpc convolutional codes from ldpc block codes"

Connected through hyperedge HE-8 (induced by source paper "asymptotic analysis and spatial coupling of counter braids"):
  with members:
    - "spatially coupled turbo like codes"
    - "deterministic and ensemble based spatially coupled product codes"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "spatially coupled ldpc codes for decode and forward relaying of two correlated sources over the bec"
    - "approaching the rate distortion limit with spatial coupling belief propagation and decimation"
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "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)