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: "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
  Abstract: The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample $k$ elements without replacement. We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search. Even for exponentially large domains, the number of model evaluations grows only linear in $k$ and the maximum sampled sequence length. The algorithm creates a theoretical connection between sampling and (deterministic) beam search and can be used as a principled intermediate alternative. In a translation task, the proposed method compares favourably against alternatives to obtain diverse yet good quality translations. We show that sequences sampled without replacement can be used to construct low-variance estimators for expected sentence-level BLEU score and model entropy.

Connected through hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases"):
  with members:
    - "dataset distillation"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "repair removing representation bias by dataset resampling"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"

Connected through hyperedge HE-2 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "topic guided variational autoencoders for text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "differentiable graph module dgm graph convolutional networks"):
  with members:
    - "brain signal classification via learning connectivity structure"
    - "graph neural networks with convolutional arma filters"
    - "link prediction based on graph neural networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adaptive graph convolutional neural networks"
    - "dual primal graph convolutional networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-4 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement"):
  with members:
    - "direct policy gradients direct optimization of policies in discrete action spaces"
    - "direct optimization through arg max for discrete variational auto encoder"
    - "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-5 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "3d aware scene manipulation via inverse graphics"
    - "a tree based decoder for neural machine translation"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

Connected through hyperedge HE-6 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "reparameterizable subset sampling via continuous relaxations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-7 (induced by source paper "incremental sampling without replacement for sequence models"):
  with members:
    - "the curious case of neural text degeneration"
    - "frangel component based synthesis with control structures"
    - "spoc search based pseudocode to code"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "attention is all you need"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"
    - "selfie self supervised pretraining for image embedding"

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)