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 sampling from the gibbs distribution with random maximum a posteriori perturbations"
  Abstract: In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low dimensional perturbations and solving the corresponding MAP assignments. Our approach also leads to new ways to derive lower bounds on partition functions. We demonstrate empirically that our method excels in the typical "high signal - high coupling" regime. The setting results in ragged energy landscapes that are challenging for alternative approaches to sampling and/or lower bounds.

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 measure concentration of 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 "direct optimization through arg max for discrete variational auto encoder"):
  with members:
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "toward controlled generation of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "learning hard alignments with variational inference"

Connected through hyperedge HE-3 (induced by source paper "approximate inference via weighted rademacher complexity"):
  with members:
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "high dimensional inference with random maximum a posteriori perturbations"
    - "provable submodular minimization using wolfe s algorithm"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "on the partition function and random maximum a posteriori perturbations"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-4 (induced by source paper "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"):
  with members:
    - "enriching word vectors with subword information"
    - "clustering is efficient for approximate maximum inner product search"
    - "learning visual features from large weakly supervised data"
    - "deep networks with large output spaces"
    - "imagenet large scale visual recognition challenge"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "perturb and mpm quantifying segmentation uncertainty in dense multi label crfs"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "structured stochastic variational inference"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-6 (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"
    - "on measure concentration of random maximum a posteriori perturbations"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "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-7 (induced by source paper "multicuts and perturb map for probabilistic graph clustering"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "higher order segmentation via multicuts"
    - "opengm a c library for discrete graphical models"
    - "on the partition function and random maximum a posteriori perturbations"
    - "large scale correlation clustering optimization"
    - "continuous multiclass labeling approaches and algorithms"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "training restricted boltzmann machine by perturbation"):
  with members:
    - "on the partition function and random maximum a posteriori perturbations"

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)