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: "the generalized area theorem and some of its consequences"
  Abstract: There is a fundamental relationship between belief propagation and maximum a posteriori decoding. The case of transmission over the binary erasure channel was investigated in detail in a companion paper. This paper investigates the extension to general memoryless channels (paying special attention to the binary case). An area theorem for transmission over general memoryless channels is introduced and some of its many consequences are discussed. We show that this area theorem gives rise to an upper-bound on the maximum a posteriori threshold for sparse graph codes. In situations where this bound is tight, the extrinsic soft bit estimates delivered by the belief propagation decoder coincide with the correct a posteriori probabilities above the maximum a posteriori threshold. More generally, it is conjectured that the fundamental relationship between the maximum a posteriori and the belief propagation decoder which was observed for transmission over the binary erasure channel carries over to the general case. We finally demonstrate that in order for the design rate of an ensemble to approach the capacity under belief propagation decoding the component codes have to be perfectly matched, a statement which is well known for the special case of transmission over the binary erasure channel.

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 "generalized ldpc codes with convolutional code constraints"):
  with members:
    - "spatially coupled turbo like codes a new trade off between waterfall and error floor"
    - "spatially coupled turbo like codes"
    - "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-3 (induced by source paper "understanding phase transitions via mutual information and mmse"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "dynamical functional theory for compressed sensing"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"

Connected through hyperedge HE-4 (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-5 (induced by source paper "multi edge type ldpc code design with g exit charts for continuous variable quantum key distribution"):
  with members:
    - "optimization of graph based codes for belief propagation decoding"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "density evolution thresholds and the stability condition for non binary ldpc codes"

Connected through hyperedge HE-6 (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"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "analysis and design of tuned turbo codes"
    - "staircase codes fec for 100 gb s otn"

Connected through hyperedge HE-7 (induced by source paper "asymptotic mutual information for the two groups stochastic block model"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral detection in the censored block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "tight error bounds for structured prediction"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"

Connected through hyperedge HE-8 (induced by source paper "reed muller codes achieve capacity on erasure channels"):
  with members:
    - "soft decision decoding of reed muller codes recursive lists"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "reed muller codes for random erasures and errors"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "cyclic coded integer forcing equalization"
    - "channel coding the road to channel capacity"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori 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)