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 "on the expressive power of deep diagonal circulant neural networks"):
  Abstract: In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which all weight matrices are the product of diagonal and circulant ones. We show that these networks outperform the recently introduced deep networks with other types of structured layers. Besides introducing principled techniques for training these models, we provide theoretical guarantees regarding their expressivity. Indeed, we prove that the function space spanned by diagonal circulant networks of bounded depth includes the one spanned by dense networks with specific properties on their rank. We conduct a thorough experimental study to compare the performance of deep diagonal circulant networks with state of the art models based on structured matrices and with dense models. We show that our models achieve better accuracy than their structured alternatives while required 2x fewer weights as the next best approach. Finally we train deep diagonal circulant networks to build a compact and accurate models on a real world video classification dataset with over 3.8 million training examples.

Includes members:
  Member "on the expressive power of deep fully circulant neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "butterfly transform an efficient fft based neural architecture design")
  Member "training compact deep learning models for video classification using circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and training deep diagonal circulant neural networks")
  Member "deep complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep quaternion features for privacy protection")
  Member "youtube 8m a large scale video classification benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "tensorizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "acdc a structured efficient linear layer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "text generation with exemplar based adaptive decoding")
  Member "compressing neural networks with the hashing trick":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a network pruning network approach to deep model compression")
  Member "an exploration of parameter redundancy in deep networks with circulant projections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "low memory neural network training a technical report")

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)