human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "predicting time varying distributions with limited training data"
  Abstract: In the task of distribution-to-distribution regression, a recently proposed model, distribution regression network (DRN) (Kou et al., 2018) has shown superior performance compared to conventional neural networks while using much fewer parameters. The key novelty of DRN is that it encodes an entire distribution in each network node, and this compact representation allows DRN to achieve better accuracies than conventional neural networks. However, the experiments in Kou et al. (2018) focused mainly on non-time series data and there is no systematic study on how DRN performs with limited annotated data. The ability to train well with limited training data is an important feature in the current supervised training paradigm. In this paper, we evaluate DRN for the new task of performing forward prediction on time sequences of probability distributions. We also perform extensive studies on how the test performance varies with the size of training data since for time series, the number of data may be limited given the seasonality of the data. In our experiments, we show that DRN requires two to five times fewer training data than neural networks to achieve similar prediction accuracies. However, DRN is a feedforward network and does not explicitly model time dependencies. Hence we propose a new recurrent architecture for DRN, named recurrent distribution regression network (RDRN). In our experiments involving prediction on sequences of distributions, RDRN and DRN outperform neural network models, with RDRN achieving similar or better accuracies than DRN.

Connected through hyperedge HE-1 (induced by source paper "a weakly supervised learning based clustering framework"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "a compact network learning model for distribution regression"
    - "deep sets"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deepsetnet predicting sets with deep neural networks"
    - "group equivariant convolutional networks"
    - "joint unsupervised learning of deep representations and image clusters"

Question: Please tell me which class the center node 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)