human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "feedforward initialization for fast inference of deep generative networks is biologically plausible"
  Abstract: We consider deep multi-layered generative models such as Boltzmann machines or Hopfield nets in which computation (which implements inference) is both recurrent and stochastic, but where the recurrence is not to model sequential structure, only to perform computation. We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values. It means that after the feedforward initialization, the recurrent network is very close to a fixed point of the network dynamics, where the energy gradient is 0. The main condition is that consecutive layers form a good auto-encoder, or more generally that different groups of inputs into the unit (in particular, bottom-up inputs on one hand, top-down inputs on the other hand) are consistent with each other, producing the same contribution into the total weighted sum of inputs. In biological terms, this would correspond to having each dendritic branch correctly predicting the aggregate input from all the dendritic branches, i.e., the soma potential. This is consistent with the prediction that the synaptic weights into dendritic branches such as those of the apical and basal dendrites of pyramidal cells are trained to minimize the prediction error made by the dendritic branch when the target is the somatic activity. Whereas previous work has shown how to achieve fast negative phase inference (when the model is unclamped) in a predictive recurrent model, this contribution helps to achieve fast positive phase inference (when the target output is clamped) in such recurrent neural models.

Connected through hyperedge HE-1 (induced by source paper "assessing the scalability of biologically motivated deep learning algorithms and architectures"):
  with members:
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "early inference in energy based models approximates back propagation"
    - "towards biologically plausible deep learning"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"
    - "difference target propagation"

Connected through hyperedge HE-2 (induced by source paper "a framework for searching for general artificial intelligence"):
  with members:
    - "progressive neural networks"
    - "convolutional neural fabrics"
    - "online adaptation of deep architectures with reinforcement learning"
    - "learning purposeful behaviour in the absence of rewards"
    - "deep vs shallow networks an approximation theory perspective"
    - "programming with a differentiable forth interpreter"
    - "why artificial intelligence needs a task theory and what it might look like"
    - "adaptive learning with robust generalization guarantees"

Connected through hyperedge HE-3 (induced by source paper "equilibrium propagation bridging the gap between energy based models and backpropagation"):
  with members:
    - "unbiased online recurrent optimization"
    - "towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity"
    - "early inference in energy based models approximates back propagation"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "why are deep nets reversible a simple theory with implications for training"
    - "training recurrent networks online without backtracking"
    - "towards biologically plausible deep learning"
    - "an objective function for stdp"

Connected through hyperedge HE-4 (induced by source paper "towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity"):
  with members:
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"

Question: Please tell me which class the center node 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)