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: "entropic causal inference"
  Abstract: We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is simple in the true causal direction. We quantify simplicity using Renyi entropy. Our main result is that, under natural assumptions, if the exogenous variable has low $H_0$ entropy (cardinality) in the true direction, it must have high $H_0$ entropy in the wrong direction. We establish several algorithmic hardness results about estimating the minimum entropy exogenous variable. We show that the problem of finding the exogenous variable with minimum entropy is equivalent to the problem of finding minimum joint entropy given $n$ marginal distributions, also known as minimum entropy coupling problem. We propose an efficient greedy algorithm for the minimum entropy coupling problem, that for $n=2$ provably finds a local optimum. This gives a greedy algorithm for finding the exogenous variable with minimum $H_1$ (Shannon Entropy). Our greedy entropy-based causal inference algorithm has similar performance to the state of the art additive noise models in real datasets. One advantage of our approach is that we make no use of the values of random variables but only their distributions. Our method can therefore be used for causal inference for both ordinal and also categorical data, unlike additive noise models.

Connected through hyperedge HE-1 (induced by source paper "minimum entropy couplings and their applications"):
  with members:
    - "strong functional representation lemma and applications to coding theorems"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "mutual information bounds via adjacency events"
    - "on the entropy of couplings"
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "entropic inequalities and marginal problems"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

Connected through hyperedge HE-2 (induced by source paper "counterfactual off policy evaluation with gumbel max structural causal models"):
  with members:
    - "representation balancing mdps for off policy policy evaluation"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "counterfactual probabilities computational methods bounds and applications"
    - "probabilities of causation bounds and identification"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-3 (induced by source paper "we are not your real parents telling causal from confounded using mdl"):
  with members:
    - "inferring deterministic causal relations"

Connected through hyperedge HE-4 (induced by source paper "innovation representation of stochastic processes with application to causal inference"):
  with members:
    - "on the universality of the logistic loss function"
    - "lossless and lossy compression of random forests"
    - "bregman divergence bounds and the universality of the logarithmic loss"
    - "large alphabet source coding using independent component analysis"
    - "learning causal graphs with small interventions"
    - "generalized independent component analysis over finite alphabets"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "on the hardness of entropy minimization and related problems"

Connected through hyperedge HE-5 (induced by source paper "playing games with bounded entropy"):
  with members:
    - "how to find a joint probability distribution of minimum entropy almost given the marginals"
    - "on the entropy of couplings"
    - "on the hardness of entropy minimization and related problems"
    - "repeated matching pennies with limited randomness"

Connected through hyperedge HE-6 (induced by source paper "how to find a joint probability distribution of minimum entropy almost given the marginals"):
  with members:
    - "on the entropy of couplings"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

Connected through hyperedge HE-7 (induced by source paper "interaction information for causal inference the case of directed triangle"):
  with members:
    - "learning network of multivariate hawkes processes a time series approach"

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)