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: "towards a biologically plausible backprop"
  Abstract: This work contributes several new elements to the quest for a biologically plausible implementation of backprop in brains. We introduce a very general and abstract framework for machine learning, in which the quantities of interest are defined implicitly through an energy function. In this framework, only one kind of neural computation is involved both for the first phase (when the prediction is made) and the second phase (after the target is revealed), like the contrastive Hebbian learning algorithm in the continuous Hopfield model for example. Contrary to automatic differentiation in computational graphs (i.e. standard backprop), there is no need for special computation in the second phase of our framework. One advantage of our framework over contrastive Hebbian learning is that the second phase corresponds to only nudging the first-phase fixed point towards a configuration that reduces prediction error. In the case of a multi-layer supervised neural network, the output units are slightly nudged towards their target, and the perturbation introduced at the output layer propagates backward in the network. The signal 'back-propagated' during this second phase actually contains information about the error derivatives, which we use to implement a learning rule proved to perform gradient descent with respect to an objective cost function.

Connected through hyperedge HE-1 (induced by source paper "sparse attentive backtracking temporal creditassignment through reminding"):
  with members:
    - "attention is all you need"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "densely connected convolutional networks"
    - "recurrent batch normalization"
    - "effective approaches to attention based neural machine translation"
    - "training recurrent networks online without backtracking"
    - "unitary evolution recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "feedforward initialization for fast inference of deep generative networks is biologically plausible"):
  with members:
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "early inference in energy based models approximates back propagation"
    - "difference target propagation"

Connected through hyperedge HE-3 (induced by source paper "an objective function for stdp"):
  with members:
    - "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"

Connected through hyperedge HE-4 (induced by source paper "stdp as presynaptic activity times rate of change of postsynaptic activity"):
  with members:
    - "early inference in energy based models approximates back propagation"

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)