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: "inferring deterministic causal relations"
  Abstract: We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

Connected through hyperedge HE-1 (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-2 (induced by source paper "neural network attributions a causal perspective"):
  with members:
    - "towards robust interpretability with self explaining neural networks"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "smoothgrad removing noise by adding noise"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "we are not your real parents telling causal from confounded using mdl"):
  with members:
    - "entropic causal inference"

Connected through hyperedge HE-4 (induced by source paper "innovation representation of stochastic processes with application to causal inference"):
  with members:
    - "on the universality of the logistic loss function"
    - "lossless and lossy compression of random forests"
    - "bregman divergence bounds and the universality of the logarithmic loss"
    - "entropic causal inference"
    - "large alphabet source coding using independent component analysis"
    - "learning causal graphs with small interventions"
    - "generalized independent component analysis over finite alphabets"
    - "distinguishing cause from effect using observational data methods and benchmarks"

Connected through hyperedge HE-5 (induced by source paper "generalization in anti causal learning"):
  with members:
    - "generalization bounds for uniformly stable algorithms"
    - "deepcoder learning to write programs"
    - "attend infer repeat fast scene understanding with generative models"
    - "large sample learning of bayesian networks is np hard"

Connected through hyperedge HE-6 (induced by source paper "analysis of cause effect inference via regression errors"):
  with members:
    - "analysis of cause effect inference by comparing regression errors"
    - "error asymmetry in causal and anticausal regression"
    - "distinguishing cause from effect using observational data methods and benchmarks"

Connected through hyperedge HE-7 (induced by source paper "causal inference via kernel deviance measures"):
  with members:
    - "causal inference by stochastic complexity"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-8 (induced by source paper "learning independent causal mechanisms"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "adversarial discriminative domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "expert gate lifelong learning with a network of experts"
    - "distinguishing cause from effect based on exogeneity"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"

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)