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: "conducting credit assignment by aligning local representations"
  Abstract: The use of back-propagation and its variants to train deep networks is often problematic for new users, with issues such as exploding gradients, vanishing gradients, and high sensitivity to weight initialization strategies often making networks difficult to train. In this paper, we present Local Representation Alignment (LRA), a training procedure that is much less sensitive to bad initializations, does not require modifications to the network architecture, and can be adapted to networks with highly nonlinear and discrete-valued activation functions. Furthermore, we show that one variation of LRA can start with a null initialization of network weights and still successfully train networks with a wide variety of nonlinearities, including tanh, ReLU-6, softplus, signum and others that are more biologically plausible. #R##N#Experiments on MNIST and Fashion MNIST validate the performance of the algorithm and show that LRA can train networks robustly and effectively, succeeding even when back-propagation fails and outperforming other alternative learning algorithms, such as target propagation and feedback alignment.

Connected through hyperedge HE-1 (induced by source paper "supervised deep similarity matching"):
  with members:
    - "decoupled greedy learning of cnns"
    - "structured and deep similarity matching via structured and deep hebbian networks"
    - "learning from brains how to regularize machines"
    - "a neural network for semi supervised learning on manifolds"
    - "greedy layerwise learning can scale to imagenet"
    - "biologically motivated algorithms for propagating local target representations"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-2 (induced by source paper "spiking neural predictive coding for continual learning from data streams"):
  with members:
    - "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"
    - "unsupervised learning by competing hidden units"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "like a baby visually situated neural language acquisition"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "putting an end to end to end gradient isolated learning of representations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "greedy layerwise learning can scale to imagenet"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "auto encoding total correlation explanation"
    - "i revnet deep invertible networks"
    - "information theoretic co training"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "biologically motivated algorithms for propagating local target representations"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "learning to adapt by minimizing discrepancy"
    - "overcoming catastrophic forgetting by incremental moment matching"

Connected through hyperedge HE-5 (induced by source paper "associated learning decomposing end to end backpropagation based on auto encoders and target propagation"):
  with members:
    - "decoupled parallel backpropagation with convergence guarantee"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "biologically motivated algorithms for propagating local target representations"
    - "deep supervised learning using local errors"
    - "dynamic routing between capsules"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning by competing hidden units"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "biologically motivated algorithms for propagating local target representations"
    - "dense associative memory is robust to adversarial inputs"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "dense associative memory for pattern recognition"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "semi supervised learning with ladder networks"
    - "difference target propagation"

Connected through hyperedge HE-7 (induced by source paper "continual learning of recurrent neural networks by locally aligning distributed representations"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "approximating real time recurrent learning with random kronecker factors"
    - "biologically motivated algorithms for propagating local target representations"
    - "lstm a search space odyssey"
    - "learning to adapt by minimizing discrepancy"
    - "learning simpler language models with the differential state framework"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-8 (induced by source paper "biologically motivated algorithms for propagating local target representations"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "towards biologically plausible deep learning"
    - "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders"
    - "how important is weight symmetry in backpropagation"
    - "adam a method for stochastic optimization"

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)