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: "distribution of mutual information"
  Abstract: The mutual information of two random variables i and j with joint probabilities t_ij is commonly used in learning Bayesian nets as well as in many other fields. The chances t_ij are usually estimated by the empirical sampling frequency n_ij/n leading to a point estimate I(n_ij/n) for the mutual information. To answer questions like "is I(n_ij/n) consistent with zero?" or "what is the probability that the true mutual information is much larger than the point estimate?" one has to go beyond the point estimate. In the Bayesian framework one can answer these questions by utilizing a (second order) prior distribution p(t) comprising prior information about t. From the prior p(t) one can compute the posterior p(t|n), from which the distribution p(I|n) of the mutual information can be calculated. We derive reliable and quickly computable approximations for p(I|n). We concentrate on the mean, variance, skewness, and kurtosis, and non-informative priors. For the mean we also give an exact expression. Numerical issues and the range of validity are discussed.

Connected through hyperedge HE-1 (induced by source paper "deepstance at semeval 2016 task 6 detecting stance in tweets using character and word level cnns"):
  with members:
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "text understanding from scratch"
    - "enhanced twitter sentiment classification using contextual information"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "robust feature selection by mutual information distributions"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "an evaluation of naive bayesian anti spam filtering"

Connected through hyperedge HE-3 (induced by source paper "bootstrap methods for the empirical study of decision making and information flows in social systems"):
  with members:
    - "robust feature selection by mutual information distributions"
    - "a family of statistical symmetric divergences based on jensen s inequality"

Connected through hyperedge HE-4 (induced by source paper "ranking by dependence a fair criteria"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-5 (induced by source paper "robust inference of trees"):
  with members:
    - "distribution of mutual information from complete and incomplete data"

Connected through hyperedge HE-6 (induced by source paper "distribution of mutual information from complete and incomplete data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "robust feature selection by mutual information distributions"
    - "robust inference of trees"
    - "bayesian treatment of incomplete discrete data applied to mutual information and feature selection"
    - "an evaluation of naive bayesian anti spam filtering"

Connected through hyperedge HE-7 (induced by source paper "bayesian treatment of incomplete discrete data applied to mutual information and feature selection"):
  with members:
    - "robust feature selection by mutual information distributions"

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)