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: "decomposition methods for large scale lp decoding"
  Abstract: When binary linear error-correcting codes are used over symmetric channels, a relaxed version of the maximum likelihood decoding problem can be stated as a linear program (LP). This LP decoder can be used to decode error-correcting codes at bit-error-rates comparable to state-of-the-art belief propagation (BP) decoders, but with significantly stronger theoretical guarantees. However, LP decoding when implemented with standard LP solvers does not easily scale to the block lengths of modern error correcting codes. In this paper we draw on decomposition methods from optimization theory, specifically the Alternating Directions Method of Multipliers (ADMM), to develop efficient distributed algorithms for LP decoding. #R##N#The key enabling technical result is a "two-slice" characterization of the geometry of the parity polytope, which is the convex hull of all codewords of a single parity check code. This new characterization simplifies the representation of points in the polytope. Using this simplification, we develop an efficient algorithm for Euclidean norm projection onto the parity polytope. This projection is required by ADMM and allows us to use LP decoding, with all its theoretical guarantees, to decode large-scale error correcting codes efficiently. #R##N#We present numerical results for LDPC codes of lengths more than 1000. The waterfall region of LP decoding is seen to initiate at a slightly higher signal-to-noise ratio than for sum-product BP, however an error floor is not observed for LP decoding, which is not the case for BP. Our implementation of LP decoding using ADMM executes as fast as our baseline sum-product BP decoder, is fully parallelizable, and can be seen to implement a type of message-passing with a particularly simple schedule.

Connected through hyperedge HE-1 (induced by source paper "admm based decoder for binary linear codes aided by deep learning"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "the admm penalized decoder for ldpc codes"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"

Connected through hyperedge HE-2 (induced by source paper "a reduced complexity projection algorithm for admm based lp decoding"):
  with members:
    - "the admm penalized decoder for ldpc codes"
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "hardware based projection onto the parity polytope and probability simplex"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-3 (induced by source paper "minimum polytope based linear programming decoder for ldpc codes via admm approach"):
  with members:
    - "the admm penalized decoder for ldpc codes"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-4 (induced by source paper "decoding reed muller codes using minimum weight parity checks"):
  with members:
    - "code design for short blocks a survey"
    - "performance comparison of short length error correcting codes"
    - "list decoding of polar codes"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "adaptive methods for linear programming decoding"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-5 (induced by source paper "a branch price and cut algorithm for optimal decoding of ldpc codes"):
  with members:
    - "nash codes for noisy channels"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "a separation algorithm for improved lp decoding of linear block codes"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-6 (induced by source paper "adaptive linear programming decoding of nonbinary linear codes over prime fields"):
  with members:
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "on the finite length scaling of ternary polar codes"
    - "low complexity lp decoding of nonbinary linear codes"
    - "mathematical programming decoding of binary linear codes theory and algorithms"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "efficient implementation of linear programming decoding"
    - "linear programming decoding of nonbinary linear codes"
    - "a separation algorithm for improved lp decoding of linear block codes"

Connected through hyperedge HE-7 (induced by source paper "global hard thresholding algorithms for joint sparse image representation and denoising"):
  with members:
    - "bethe admm for tree decomposition based parallel map inference"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "towards a mathematical theory of super resolution"
    - "guaranteed rank minimization via singular value projection"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "parameter free ell_p box decoding of ldpc codes"):
  with members:
    - "ell_p box admm a versatile framework for integer programming"

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)