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: "set aggregation network for structured data processing"
  Abstract: We study the problem of processing structured data such as images or graphs by deep neural networks. Given a set of features extracted by convolutional or recurrent network, a global pooling is commonly applied to produce a fixed-length representation, which is subsequently used by fully connected network. Based on recent DeepSets architecture proposed by Zaheer et al. (NIPS 2017), we propose set aggregation network (SAN) as an alternative to pooling operation. In contrast to global pooling, SAN allows to embed a given set of features to a vector representation of arbitrary size. In consequence, by adjusting the size of embedding, SAN is capable of preserving the whole information from the input, which is also proven theoretically. In experiments, we demonstrate that replacing global pooling by SAN leads to the improvement of classification accuracy on various types of networks. Moreover, it is less prone to overfitting and can be used as a regularizer.

Connected through hyperedge HE-1 (induced by source paper "one element batch training by moving window"):
  with members:
    - "sliced generative models"
    - "generative adversarial networks recent developments"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "pioneer networks progressively growing generative autoencoder"
    - "cramer wold autoencoder"
    - "revisiting small batch training for deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)