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: "equivalence of equilibrium propagation and recurrent backpropagation"
  Abstract: Recurrent Backpropagation and Equilibrium Propagation are algorithms for fixed point recurrent neural networks which differ in their second phase. In the first phase, both algorithms converge to a fixed point which corresponds to the configuration where the prediction is made. In the second phase, Recurrent Backpropagation computes error derivatives whereas Equilibrium Propagation relaxes to another nearby fixed point. In this work we establish a close connection between these two algorithms. We show that, at every moment in the second phase, the temporal derivatives of the neural activities in Equilibrium Propagation are equal to the error derivatives computed iteratively in Recurrent Backpropagation. This work shows that it is not required to have a special network for the computation of error derivatives, and gives support to the hypothesis that, in biological neural networks, temporal derivatives of neural activities may code for error signals.

Connected through hyperedge HE-1 (induced by source paper "backprop diffusion is biologically plausible"):
  with members:
    - "spatiotemporal local propagation"
    - "convolutional networks in visual environments"
    - "cognitive action laws the case of visual features"
    - "motion invariance in visual environments"

Connected through hyperedge HE-2 (induced by source paper "updates of equilibrium prop match gradients of backprop through time in an rnn with static input"):
  with members:
    - "generalization of equilibrium propagation to vector field dynamics"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"

Connected through hyperedge HE-3 (induced by source paper "generalization of equilibrium propagation to vector field dynamics"):
  with members:
    - "towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "a biologically plausible learning rule for deep learning in the brain"):
  with members:
    - "biologically plausible deep learning"
    - "cyclical learning rates for training neural networks"
    - "striving for simplicity the all convolutional net"

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)