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: "an improved three weight message passing algorithm"
  Abstract: We describe how the powerful "Divide and Concur" algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message-passing algorithm based on ADMM/DC by introducing three distinct weights for messages, with "certain" and "no opinion" weights, as well as the standard weight used in ADMM/DC. The "certain" messages allow our improved algorithm to implement constraint propagation as a special case, while the "no opinion" messages speed convergence for some problems by making the algorithm focus only on active constraints. We describe how our three-weight version of ADMM/DC can give greatly improved performance for non-convex problems such as circle packing and solving large Sudoku puzzles, while retaining the exact performance of ADMM for convex problems. We also describe the advantages of our algorithm compared to other message-passing algorithms based upon belief propagation.

Connected through hyperedge HE-1 (induced by source paper "a distributed admm approach to informative trajectory planning for multi target tracking"):
  with members:
    - "potential game based non myopic sensor network planning for multi target tracking"

Connected through hyperedge HE-2 (induced by source paper "extremely low bit neural network squeeze the last bit out with admm"):
  with members:
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ternary weight networks"

Connected through hyperedge HE-3 (induced by source paper "optimal control of linear systems with limited control actions threshold based event triggered control"):
  with members:
    - "multiple loop self triggered model predictive control for network scheduling and control"
    - "sparse packetized predictive control for networked control over erasure channels"
    - "sparse representations for packetized predictive networked control"

Connected through hyperedge HE-4 (induced by source paper "the admm penalized decoder for ldpc codes"):
  with members:
    - "hierarchical and high girth qc ldpc codes"
    - "linear programming decoding of spatially coupled codes"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "mathematical programming decoding of binary linear codes theory and algorithms"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "instantons causing iterative decoding to cycle"

Connected through hyperedge HE-5 (induced by source paper "fast and effective algorithms for symmetric nonnegative matrix factorization"):
  with members:
    - "bethe admm for tree decomposition based parallel map inference"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "decomposition methods for large scale lp decoding"
    - "a tutorial on spectral clustering"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "testing fine grained parallelism for the admm on a factor graph"):
  with members:
    - "snapvx a network based convex optimization solver"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "high speed compressed sensing reconstruction in dynamic parallel mri using augmented lagrangian and parallel processing"

Connected through hyperedge HE-7 (induced by source paper "proximal operators for multi agent path planning"):
  with members:
    - "a message passing algorithm for multi agent trajectory planning"
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "a message passing algorithm for multi agent trajectory planning"):
  with members:
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

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.AI(Artificial Intelligence)