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 "recurrent deep embedding networks for genotype clustering and ethnicity prediction"):
  Abstract: The understanding of variations in genome sequences assists us in identifying people who are predisposed to common diseases, solving rare diseases, and finding the corresponding population group of the individuals from a larger population group. Although classical machine learning techniques allow researchers to identify groups (i.e. clusters) of related variables, the accuracy, and effectiveness of these methods diminish for large and high-dimensional datasets such as the whole human genome. On the other hand, deep neural network architectures (the core of deep learning) can better exploit large-scale datasets to build complex models. In this paper, we use the K-means clustering approach for scalable genomic data analysis aiming towards clustering genotypic variants at the population scale. Finally, we train a deep belief network (DBN) for predicting the geographic ethnicity. We used the genotype data from the 1000 Genomes Project, which covers the result of genome sequencing for 2504 individuals from 26 different ethnic origins and comprises 84 million variants. Our experimental results, with a focus on accuracy and scalability, show the effectiveness and superiority compared to the state-of-the-art.

Includes members:
  Member "clustering with deep learning taxonomy and new methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "memory based graph networks")
  Member "discriminatively boosted image clustering with fully convolutional auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep amortized clustering")
  Member "unsupervised deep embedding for clustering analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)