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 "splash user friendly programming interface for parallelizing stochastic algorithms"):
  Abstract: Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems. Splash consists of a programming interface and an execution engine. Using the programming interface, the user develops sequential stochastic algorithms without concerning any detail about distributed computing. The algorithm is then automatically parallelized by a communication-efficient execution engine. We provide theoretical justifications on the optimal rate of convergence for parallelizing stochastic gradient descent. Splash is built on top of Apache Spark. The real-data experiments on logistic regression, collaborative filtering and topic modeling verify that Splash yields order-of-magnitude speedup over single-thread stochastic algorithms and over state-of-the-art implementations on Spark.

Includes members:
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "communication efficient distributed dual coordinate ascent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic federated learning")
  Member "same but different fast and high quality gibbs parameter estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "heron inference for bayesian graphical models")
  Member "expectation propagation for approximate bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "mli an api for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tf replicator distributed machine learning for researchers")
  Member "bpr bayesian personalized ranking from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "making gradient descent optimal for strongly convex stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless federated learning with local differential privacy")
  Member "online importance weight aware updates":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "warm starting contextual bandits robustly combining supervised and bandit feedback")

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.LG(Machine Learning)