human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distributed bayesian piecewise sparse linear models"):
  Abstract: The importance of interpretability of machine learning models has been increasing due to emerging enterprise predictive analytics, threat of data privacy, accountability of artificial intelligence in society, and so on. Piecewise linear models have been actively studied to achieve both accuracy and interpretability. They often produce competitive accuracy against state-of-the-art non-linear methods. In addition, their representations (i.e., rule-based segmentation plus sparse linear formula) are often preferred by domain experts. A disadvantage of such models, however, is high computational cost for simultaneous determinations of the number of "pieces" and cardinality of each linear predictor, which has restricted their applicability to middle-scale data sets. This paper proposes a distributed factorized asymptotic Bayesian (FAB) inference of learning piece-wise sparse linear models on distributed memory architectures. The distributed FAB inference solves the simultaneous model selection issue without communicating $O(N)$ data where N is the number of training samples and achieves linear scale-out against the number of CPU cores. Experimental results demonstrate that the distributed FAB inference achieves high prediction accuracy and performance scalability with both synthetic and benchmark data.

Includes members:
  Member "mobile big data analytics using deep learning and apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal pricing of internet of things a machine learning approach")
  Member "rebuilding factorized information criterion asymptotically accurate marginal likelihood":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tractable fully bayesian method for the stochastic block model")
  Member "alid scalable dominant cluster detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mining density contrast subgraphs")
  Member "mli an api for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tf replicator distributed machine learning for researchers")
  Member "factorized asymptotic bayesian hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rebuilding factorized information criterion asymptotically accurate marginal likelihood")
  Member "a system for induction of oblique decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards interpretable deep neural networks an exact transformation to multi class multivariate decision trees")

Question: Please tell me which class the source paper 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)