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: "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"
  Abstract: We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N) + 1 experiments are sufficient and in the worst case necessary to determine the causal relations among N >= 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 < K < 1/(2N) we provide an upper bound on the number experiments required to determine causal structure when each experiment simultaneously randomizes K variables. For large N, these bounds are significantly lower than the N - 1 bound required when each experiment randomizes at most one variable. For kmax < N/2, we show that (N/kmax-1)+N/(2kmax)log2(kmax) experiments aresufficient and in the worst case necessary. We over a conjecture as to the minimal number of experiments that are in the worst case sufficient to identify all causal relations among N observed variables that are a subset of the vertices of a DAG.

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"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"

Connected through hyperedge HE-2 (induced by source paper "strategic classification is causal modeling in disguise"):
  with members:
    - "optimal decision making under strategic behavior"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"

Connected through hyperedge HE-3 (induced by source paper "computationally and statistically efficient learning of causal bayes nets using path queries"):
  with members:
    - "reconstructing a bounded degree directed tree using path queries"
    - "learning causal graphs with small interventions"
    - "powers of tensors and fast matrix multiplication"
    - "sparsityboost a new scoring function for learning bayesian network structure"

Connected through hyperedge HE-4 (induced by source paper "dis entangling mixture of interventions on a causal bayesian network using aggregate observations"):
  with members:
    - "causal discovery from changes"
    - "learning mixtures of dag models"

Connected through hyperedge HE-5 (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"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal networks semantics and expressiveness"

Connected through hyperedge HE-6 (induced by source paper "experimental design for cost aware learning of causal graphs"):
  with members:
    - "budgeted experiment design for causal structure learning"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning gaussian networks"

Connected through hyperedge HE-7 (induced by source paper "counting and uniform sampling from markov equivalent dags"):
  with members:
    - "budgeted experiment design for causal structure learning"
    - "causal inference and causal explanation with background knowledge"

Connected through hyperedge HE-8 (induced by source paper "a primer on causal analysis"):
  with members:
    - "distinguishing cause from effect using observational data methods and benchmarks"

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)