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 "convolutional bipartite attractor networks"):
  Abstract: In human perception and cognition, the fundamental operation that brains perform is interpretation: constructing coherent neural states from noisy, incomplete, and intrinsically ambiguous evidence. The problem of interpretation is well matched to an early and often overlooked architecture, the attractor network---a recurrent neural network that performs constraint satisfaction, imputation of missing features, and clean up of noisy data via energy minimization dynamics. We revisit attractor nets in light of modern deep learning methods, and propose a convolutional bipartite architecture with a novel training loss, activation function, and connectivity constraints. We tackle problems much larger than have been previously explored with attractor nets and demonstrate their potential for image denoising, completion, and super-resolution. We argue that this architecture is better motivated than ever-deeper feedforward models and is a viable alternative to more costly sampling-based methods on a range of supervised and unsupervised tasks.

Includes members:
  Member "implicit generation and generalization in energy based models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "learning attractor dynamics for generative memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "product kanerva machines factorized bayesian memory")
  Member "reviving and improving recurrent back propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability")
  Member "fast and accurate image super resolution with deep laplacian pyramid networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "density estimation using real nvp":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "dense associative memory for pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypercomplex valued recurrent correlation neural networks")
  Member "deeply recursive convolutional network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)