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: "joint causal inference from multiple contexts"
  Abstract: The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets that elegantly unifies both approaches. JCI is a causal modeling approach rather than a specific algorithm, and it can be used in combination with any causal discovery algorithm that can take into account certain background knowledge. The main idea is to reduce causal discovery from multiple datasets originating from different contexts (e.g., different experimental conditions) to causal discovery from a single pooled dataset by adding auxiliary context variables and incorporating applicable background knowledge on the causal relationships involving the context variables. We propose different flavours of JCI that differ in the amount of background knowledge that is assumed. JCI can deal with several different types of interventions in a unified fashion, does not require knowledge on intervention targets or types in case of interventional data, and allows one to fully exploit all the information in the joint distribution on system and context variables. We explain how some well-known causal discovery algorithms can be seen as implementations of the JCI framework, but we also propose novel implementations that are simple adaptations of existing causal discovery methods for purely observational data to the JCI setting. We evaluate different implementations of the JCI approach on synthetic data and on flow cytometry protein expression data and conclude that JCI implementations can outperform state-of-the-art causal discovery algorithms.

Connected through hyperedge HE-1 (induced by source paper "dag gnn dag structure learning with graph neural networks"):
  with members:
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "adam a method for stochastic optimization"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "data analysis with bayesian networks a bootstrap approach"
    - "spectral networks and locally connected networks on graphs"
    - "causal inference in the presence of latent variables and selection bias"
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-2 (induced by source paper "fair predictors under distribution shift"):
  with members:
    - "transfer of machine learning fairness across domains"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-3 (induced by source paper "causal transfer learning"):
  with members:
    - "ancestral causal inference"
    - "causal discovery from changes"
    - "a general algorithm for deciding transportability of experimental results"
    - "cyclic causal discovery from continuous equilibrium data"
    - "on causal and anticausal learning"
    - "domain adaptation learning bounds and algorithms"

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)