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: "a survey of learning causality with data problems and methods"
  Abstract: The era of big data provides researchers with convenient access to copious data. However, people often have little knowledge about it. The increasing prevalence of big data is challenging the traditional methods of learning causality because they are developed for the cases with limited amount of data and solid prior causal knowledge. This survey aims to close the gap between big data and learning causality with a comprehensive and structured review of traditional and frontier methods and a discussion about some open problems of learning causality. We begin with preliminaries of learning causality. Then we categorize and revisit methods of learning causality for the typical problems and data types. After that, we discuss the connections between learning causality and machine learning. At the end, some open problems are presented to show the great potential of learning causality with data.

Connected through hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "explainable reinforcement learning through a causal lens"
    - "learning interpretable models with causal guarantees"
    - "explaining classifiers with causal concept effect cace"
    - "neural network attributions a causal perspective"
    - "on the accuracy of influence functions for measuring group effects"
    - "explaining deep learning models with constrained adversarial examples"

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 "the many faces of deep learning"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "deep reinforcement learning a brief survey"
    - "overcoming catastrophic forgetting in neural networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "building machines that learn and think like people"
    - "deep learning in neural networks"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (induced by source paper "learning individual causal effects from networked observational data"):
  with members:
    - "learning counterfactual representations for estimating individual dose response curves"
    - "using network motifs to characterize temporal network evolution leading to diffusion inhibition"
    - "graph convolutional neural networks for web scale recommender systems"
    - "linked causal variational autoencoder for inferring paired spillover effects"
    - "perfect match a simple method for learning representations for counterfactual inference with neural networks"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "confounding robust policy improvement"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "causality based feature selection methods and evaluations"):
  with members:
    - "discovering markov blanket from multiple interventional datasets"
    - "a unified view of causal and non causal feature selection"
    - "feature selection a data perspective"
    - "learning identifiable gaussian bayesian networks in polynomial time and sample complexity"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "forward backward selection with early dropping"
    - "challenges of feature selection for big data analytics"
    - "scalable and accurate online feature selection for big data"

Connected through hyperedge HE-6 (induced by source paper "counterfactual evaluation of treatment assignment functions with networked observational data"):
  with members:
    - "learning individual treatment effects from networked observational data"
    - "learning individual causal effects from networked observational data"
    - "perfect match a simple method for learning representations for counterfactual inference with neural networks"
    - "recommendations as treatments debiasing learning and evaluation"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-7 (induced by source paper "towards a framework for observational causality from time series when shannon meets turing"):
  with members:
    - "interaction information for causal inference the case of directed triangle"
    - "causal network inference by optimal causation entropy"
    - "dynamic network models for forecasting"
    - "nonnegative decomposition of multivariate information"

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.AI(Artificial Intelligence)