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: "exploiting causal independence in bayesian network inference"
  Abstract: A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.

Connected through hyperedge HE-1 (induced by source paper "an advance on variable elimination with applications to tensor based computation"):
  with members:
    - "efficient contraction of large tensor networks for weighted model counting through graph decompositions"
    - "on the relative expressiveness of bayesian and neural networks"
    - "on relaxing determinism in arithmetic circuits"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "group emotion recognition using machine learning"):
  with members:
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "emotion recognition in the wild using deep neural networks and bayesian classifiers"
    - "ssd single shot multibox detector"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "query the model precomputations for efficient inference with bayesian networks"):
  with members:
    - "a simple cache model for image recognition"
    - "generative neural machine translation"
    - "database learning toward a database that becomes smarter every time"
    - "d separation from theorems to algorithms"

Connected through hyperedge HE-4 (induced by source paper "from high level inference algorithms to efficient code"):
  with members:
    - "autoconj recognizing and exploiting conjugacy without a domain specific language"
    - "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories"
    - "swift compiled inference for probabilistic programming languages"
    - "probabilistic inference modulo theories"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "dynamic blocking and collapsing for gibbs sampling"
    - "church a language for generative models"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "on the relative expressiveness of bayesian and neural networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "human level intelligence or animal like abilities"
    - "bounding and counting linear regions of deep neural networks"
    - "a differential approach to inference in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-6 (induced by source paper "hypertree decompositions revisited for pgms"):
  with members:
    - "size bounds and query plans for relational joins"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"
    - "join graph propagation algorithms"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-7 (induced by source paper "juggling functions inside a database"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"
    - "flexible caching in trie joins"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"

Connected through hyperedge HE-8 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"

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)