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 "dpsom deep probabilistic clustering with self organizing maps"):
  Abstract: Generating visualizations and interpretations from high-dimensional data is a common problem in many applications. Two key approaches for tackling this problem are clustering and representation learning. On the one hand, there are very performant deep clustering models, such as DEC and IDEC. On the other hand, there are interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps. However, current methods do not yet successfully combine these two approaches. We present a novel way to fit self-organizing maps with probabilistic cluster assignments, PSOM, a new deep architecture for probabilistic clustering, DPSOM, and its extension to time series data, T-DPSOM. We show that they achieve superior clustering performance compared to current deep clustering methods on static MNIST/Fashion-MNIST data as well as medical time series, while also inducing an interpretable representation. Moreover, on medical time series, T-DPSOM successfully predicts future trajectories in the original data space.

Includes members:
  Member "unsupervised scalable representation learning for multivariate time series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning for few shot time series classification")
  Member "generating diverse high fidelity images with vq vae 2":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "clustering with deep learning taxonomy and new methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "the loracs prior for vaes letting the trees speak for the data":
    also connected through other hyperedges:
      (none)
  Member "som vae interpretable discrete representation learning on time series":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")
  Member "neural discrete representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "towards k means friendly spaces simultaneous deep learning and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics")
  Member "unsupervised deep embedding for clustering analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")

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)