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: "bethe admm for tree decomposition based parallel map inference"
  Abstract: We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.

Connected through hyperedge HE-1 (induced by source paper "map inference via l2 sphere linear program reformulation"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "efficiently searching for frustrated cycles in map inference"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-2 (induced by source paper "global hard thresholding algorithms for joint sparse image representation and denoising"):
  with members:
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "towards a mathematical theory of super resolution"
    - "decomposition methods for large scale lp decoding"
    - "guaranteed rank minimization via singular value projection"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "fast and effective algorithms for symmetric nonnegative matrix factorization"):
  with members:
    - "an improved three weight message passing algorithm"
    - "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-4 (induced by source paper "getting feasible variable estimates from infeasible ones mrf local polytope study"):
  with members:
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "opengm a c library for discrete graphical models"
    - "tightening lp relaxations for map using message passing"
    - "a new class of upper bounds on the log partition function"
    - "tightening fractional covering upper bounds on the partition function for high order region graphs"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-5 (induced by source paper "online alternating direction method"):
  with members:
    - "projection free online learning"
    - "decomposition methods for large scale lp decoding"
    - "trading regret for efficiency online convex optimization with long term constraints"
    - "sparse inverse covariance selection via alternating linearization methods"
    - "optimal distributed online prediction using mini batches"
    - "robust principal component analysis"

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)