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 "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.

Includes members:
  Member "universal approximations of permutation invariant equivariant functions by deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "set2graph learning graphs from sets")
  Member "regularized evolution for image classifier architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "searching for winograd aware quantized networks")
  Member "how powerful are graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "janossy pooling learning deep permutation invariant functions for variable size inputs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "junction tree variational autoencoder for molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmarking graph neural networks")
  Member "processing of missing data by neural networks":
    also connected through other hyperedges:
      (none)
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)