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: "learning independent causal mechanisms"
  Abstract: Independent causal mechanisms are a central concept in the study of causality with implications for machine learning tasks. In this work we develop an algorithm to recover a set of (inverse) independent mechanisms relating a distribution transformed by the mechanisms to a reference distribution. The approach is fully unsupervised and based on a set of experts that compete for data to specialize and extract the mechanisms. We test and analyze the proposed method on a series of experiments based on image transformations. Each expert successfully maps a subset of the transformed data to the original domain, and the learned mechanisms generalize to other domains. We discuss implications for domain transfer and links to recent trends in generative modeling.

Connected through hyperedge HE-1 (induced by source paper "recurrent independent mechanisms"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "modular networks learning to decompose neural computation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"

Connected through hyperedge HE-2 (induced by source paper "causality for machine learning"):
  with members:
    - "optimal decision making under strategic behavior"
    - "recurrent independent mechanisms"
    - "learning robust representations by projecting superficial statistics out"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "causal reasoning from meta reinforcement learning"
    - "competitive training of mixtures of independent deep generative models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"

Connected through hyperedge HE-3 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-4 (induced by source paper "modular meta learning with shrinkage"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "optimizing millions of hyperparameters by implicit differentiation"
    - "meta learning with differentiable convex optimization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning with competitive ensembles of information constrained primitives"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "model primitive hierarchical lifelong reinforcement learning"
    - "hierarchical visuomotor control of humanoids"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "neural probabilistic motor primitives for humanoid control"
    - "inferring and executing programs for visual reasoning"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-6 (induced by source paper "fast and flexible image blind denoising via competition of experts"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "multi source domain adaptation with mixture of experts"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "stochastic multiple choice learning for training diverse deep ensembles"

Connected through hyperedge HE-7 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms"):
  with members:
    - "on first order meta learning algorithms"
    - "causal reasoning from meta reinforcement learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "modular meta learning"
    - "the consciousness prior"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-8 (induced by source paper "ensembles of locally independent prediction models"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"
    - "how complex is your classification problem a survey on measuring classification complexity"
    - "learning qualitatively diverse and interpretable rules for classification"
    - "snapshot ensembles train 1 get m for free"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "intriguing properties of neural networks"

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)