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: "an algorithm for deciding if a set of observed independencies has a causal explanation"
  Abstract: In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies. Formally, given a list M of conditional independence statements, it is required to decide whether there exists a directed acyclic graph (dag) D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via dseparation in D. We present and analyze an effective algorithm that tests for the existence of such a day, and produces one, if it exists.

Connected through hyperedge HE-1 (induced by source paper "interventional experiment design for causal structure learning"):
  with members:
    - "experimental design for cost aware learning of causal graphs"
    - "counting and uniform sampling from markov equivalent dags"
    - "counting and sampling from markov equivalent dags using clique trees"
    - "budgeted experiment design for causal structure learning"
    - "cost optimal learning of causal graphs"
    - "learning causal graphs with small interventions"
    - "lazier than lazy greedy"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-2 (induced by source paper "what do language representations really represent"):
  with members:
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning language representations for typology prediction"
    - "probabilistic typology deep generative models of vowel inventories"
    - "continuous multilinguality with language vectors"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"

Connected through hyperedge HE-3 (induced by source paper "learning and testing causal models with interventions"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "testing bayesian networks"
    - "optimal testing for properties of distributions"
    - "causal networks semantics and expressiveness"
    - "inequality constraints in causal models with hidden variables"

Connected through hyperedge HE-4 (induced by source paper "budgeted experiment design for causal structure learning"):
  with members:
    - "counting and uniform sampling from markov equivalent dags"
    - "learning causal structures using regression invariance"
    - "cost optimal learning of causal graphs"
    - "learning causal graphs with small interventions"
    - "causal network inference by optimal causation entropy"
    - "causal discovery from a mixture of experimental and observational data"
    - "directed information graphs"
    - "bayesian model averaging using the k best bayesian network structures"

Connected through hyperedge HE-5 (induced by source paper "learning causal structures using regression invariance"):
  with members:
    - "optimal experiment design for causal discovery from fixed number of experiments"
    - "interaction information for causal inference the case of directed triangle"
    - "discovery and visualization of nonstationary causal models"
    - "learning causal graphs with small interventions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning causal graphs with small interventions"):
  with members:
    - "strong completeness and faithfulness in bayesian networks"
    - "causal inference and causal explanation with background knowledge"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-7 (induced by source paper "enumerating markov equivalence classes of acyclic digraph models"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "operations for learning with graphical models"

Connected through hyperedge HE-8 (induced by source paper "a transformational characterization of equivalent bayesian network structures"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal inference and causal explanation with background knowledge"
    - "using causal information and local measures to learn bayesian 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.AI(Artificial Intelligence)