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 "effect of top down connections in hierarchical sparse coding"):
  Abstract: Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers. In this study, a new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to assess the impact of this inter-layer feedback connection. In particular, the 2L-SPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of independent Lasso layers. The 2L-SPC and the 2-layers Hi-La networks are trained on 4 different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the 2L-SPC is faster to converge than for the Hi-La model. Third, we show that the 2L-SPC also accelerates the learning process. Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the 2L-SPC features are more generic and informative.

Includes members:
  Member "deep predictive coding network for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer")
  Member "multilayer convolutional sparse modeling pursuit and dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors")
  Member "deep predictive coding network with local recurrent processing for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prednet and predictive coding a critical review")
  Member "deep predictive coding networks for video prediction and unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "winner take all autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "k sparse autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "human to ai coach improving human inputs to ai systems")
  Member "convolutional matching pursuit and dictionary training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse deep predictive coding captures contour integration capabilities of the early visual system")

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.CV(Computer Vision and Pattern Recognition)