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 "equilibrium propagation bridging the gap between energy based models and backpropagation"):
  Abstract: We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point, or stationary distribution) towards a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged towards their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal 'back-propagated' during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not.

Includes members:
  Member "unbiased online recurrent optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability")
  Member "towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalization of equilibrium propagation to vector field dynamics")
  Member "feedforward initialization for fast inference of deep generative networks is biologically plausible":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assessing the scalability of biologically motivated deep learning algorithms and architectures")
  Member "early inference in energy based models approximates back propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep neural networks abstract like humans")
  Member "stdp as presynaptic activity times rate of change of postsynaptic activity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boltzmann machines for time series")
  Member "why are deep nets reversible a simple theory with implications for training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "training recurrent networks online without backtracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the power of linear recurrent neural networks")
  Member "towards biologically plausible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sideways depth parallel training of video models")

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)