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: "interaction information for causal inference the case of directed triangle"
  Abstract: Interaction information is one of the multivariate generalizations of mutual information, which expresses the amount information shared among a set of variables, beyond the information, which is shared in any proper subset of those variables. Unlike (conditional) mutual information, which is always non-negative, interaction information can be negative. We utilize this property to find the direction of causal influences among variables in a triangle topology under some mild assumptions.

Connected through hyperedge HE-1 (induced by source paper "towards a framework for observational causality from time series when shannon meets turing"):
  with members:
    - "a survey of learning causality with data problems and methods"
    - "causal network inference by optimal causation entropy"
    - "dynamic network models for forecasting"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-2 (induced by source paper "excessive invariance causes adversarial vulnerability"):
  with members:
    - "adversarial vision challenge"
    - "analyzing inverse problems with invertible neural networks"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "adversarial vulnerability for any classifier"
    - "invertible residual networks"
    - "i revnet deep invertible networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "learning causal structures using regression invariance"):
  with members:
    - "optimal experiment design for causal discovery from fixed number of experiments"
    - "discovery and visualization of nonstationary causal models"
    - "learning causal graphs with small interventions"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal discovery from changes"
    - "inferring deterministic causal relations"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

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)