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 "ds mlr exploiting double separability for scaling up distributed multinomial logistic regression"):
  Abstract: Multinomial logistic regression is a popular tool in the arsenal of machine learning algorithms, yet scaling it to datasets with very large number of data points and classes has not been trivial. This is primarily because one needs to compute the log-partition function on every data point. This makes distributing the computation hard. In this paper, we present a distributed stochastic gradient descent based optimization method (DS-MLR) for scaling up multinomial logistic regression problems to very large data. Our algorithm exploits double-separability, an attractive property we observe in the objective functions of several models in machine learning, that allows us to distribute both data and model parameters simultaneously across multiple machines. In addition to being easily parallelizable, our algorithm achieves good test accuracy within a short period of time, with a low overall time and memory footprint as demonstrated by empirical results on both single and multi-machine settings. For instance, on a dataset with 93,805 training instances and 12,294 classes, we achieve close to optimal f-score in 10,000 seconds using 2 machines each having 12 cores.

Includes members:
  Member "lshtc a benchmark for large scale text classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "distributed machine learning via sufficient factor broadcasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on distributed machine learning")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning on volatile instances")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)