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: "on measure concentration of random maximum a posteriori perturbations"
  Abstract: The maximum a-posteriori (MAP) perturbation framework has emerged as a useful approach for inference and learning in high dimensional complex models. By maximizing a randomly perturbed potential function, MAP perturbations generate unbiased samples from the Gibbs distribution. Unfortunately, the computational cost of generating so many high-dimensional random variables can be prohibitive. More efficient algorithms use sequential sampling strategies based on the expected value of low dimensional MAP perturbations. This paper develops new measure concentration inequalities that bound the number of samples needed to estimate such expected values. Applying the general result to MAP perturbations can yield a more efficient algorithm to approximate sampling from the Gibbs distribution. The measure concentration result is of general interest and may be applicable to other areas involving expected estimations.

Connected through hyperedge HE-1 (induced by source paper "learning with differentiable perturbed optimizers"):
  with members:
    - "differentiable convex optimization layers"
    - "differentiation of blackbox combinatorial solvers"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "online linear optimization via smoothing"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "on the partition function and random maximum a posteriori perturbations"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-2 (induced by source paper "high dimensional inference with random maximum a posteriori perturbations"):
  with members:
    - "direct optimization through arg max for discrete variational auto encoder"
    - "approximate inference via weighted rademacher complexity"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-3 (induced by source paper "a comparative study of modern inference techniques for structured discrete energy minimization problems"):
  with members:
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "opengm a c library for discrete graphical models"
    - "getting feasible variable estimates from infeasible ones mrf local polytope study"
    - "efficiently searching for frustrated cycles in map inference"
    - "submodular decomposition framework for inference in associative markov networks with global constraints"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-4 (induced by source paper "on sampling from the gibbs distribution with random maximum a posteriori perturbations"):
  with members:
    - "negative tree reweighted belief propagation"
    - "on the partition function and random maximum a posteriori perturbations"
    - "tightening lp relaxations for map using message passing"
    - "a new class of upper bounds on the log partition function"
    - "the bethe partition function of log supermodular graphical models"
    - "approximating partition functions of two state spin systems"

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.LG(Machine Learning)