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: "direct optimization through arg max for discrete variational auto encoder"
  Abstract: Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. Our work optimizes the discrete VAE objective directly, using its Gumbel-Max reparameterization, by applying the direct loss minimization technique to generative models. This optimization technique propagates gradients through the reparameterized $\arg \max$, which are estimated by the difference of gradients of two $\arg \max$ predictions. This realization provides the means to learn latent representations in cases when evaluating the $\arg \max$ operation is tractable while evaluating the softmax operation is intractable.

Connected through hyperedge HE-1 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "direct policy gradients direct optimization of policies in discrete action spaces"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural machine translation with gumbel greedy decoding"
    - "classical structured prediction losses for sequence to sequence learning"
    - "searnn training rnns with global local losses"

Connected through hyperedge HE-2 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"

Connected through hyperedge HE-3 (induced by source paper "direct policy gradients direct optimization of policies in discrete action spaces"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "variational bayesian reinforcement learning with regret bounds"
    - "particle value functions"
    - "guided policy search as approximate mirror descent"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "path integral guided policy search"

Connected through hyperedge HE-4 (induced by source paper "explaining classifiers with causal concept effect cace"):
  with members:
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "counterfactual visual explanations"
    - "learning not to learn training deep neural networks with biased data"
    - "generating counterfactual explanations with natural language"
    - "explaining image classifiers by counterfactual generation"
    - "smoothgrad removing noise by adding noise"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "causalgan learning causal implicit generative models with adversarial training"

Connected through hyperedge HE-5 (induced by source paper "high dimensional inference with random maximum a posteriori perturbations"):
  with members:
    - "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"
    - "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"

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)