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 "accelerating deep learning with memcomputing"):
  Abstract: Restricted Boltzmann machines (RBMs) and their extensions, often called "deep-belief networks", are very powerful neural networks that have found widespread applicability in the fields of machine learning and big data. The standard way to training these models resorts to an iterative unsupervised procedure based on Gibbs sampling, called "contrastive divergence", and additional supervised tuning via back-propagation. However, this procedure has been shown not to follow any gradient and can lead to suboptimal solutions. In this paper, we show a very efficient alternative to contrastive divergence by means of simulations of digital memcomputing machines (DMMs). We test our approach on pattern recognition using the standard MNIST data set of hand-written numbers. DMMs sample very effectively the vast phase space defined by the probability distribution of RBMs over the test sample inputs, and provide a very good approximation close to the optimum. This efficient search significantly reduces the number of generative pre-training iterations necessary to achieve a given level of accuracy in the MNIST data set, as well as a total performance gain over the traditional approaches. In fact, the acceleration of the pre-training achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set. Notably, however, DMMs perform far better than the reported quantum annealing results in terms of quality of the training. Our approach is agnostic about the connectivity of the network. Therefore, it can be extended to train full Boltzmann machines, and even deep networks at once.

Includes members:
  Member "instantons in self organizing logic gates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aircraft loading optimization memcomputing the 5th airbus problem")
  Member "weighted contrastive divergence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "snn under attack are spiking deep belief networks vulnerable to adversarial examples")
  Member "perspective memcomputing leveraging memory and physics to compute efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "p bits for probabilistic spin logic")
  Member "memcomputing numerical inversion with self organizing logic gates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "instantons in self organizing logic gates")
  Member "evidence of an exponential speed up in the solution of hard optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stress testing memcomputing on hard combinatorial optimization problems")
  Member "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "p bits for probabilistic spin logic")
  Member "universal memcomputing machines":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a scalable photonic computer solving the subset sum problem")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)