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: "biologically motivated algorithms for propagating local target representations"
  Abstract: Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologically-motivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmarks, we find that both of our proposed algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.

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"
    - "conducting credit assignment by aligning local 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 "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"
    - "conducting credit assignment by aligning local 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-4 (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"
    - "conducting credit assignment by aligning local 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-5 (induced by source paper "the neural state pushdown automata"):
  with members:
    - "on the computational power of rnns"
    - "learning by abstraction the neural state machine"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "context free transductions with neural stacks"
    - "number sequence prediction problems for evaluating computational powers of neural networks"
    - "evaluating the ability of lstms to learn context free grammars"
    - "unbiased online recurrent optimization"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

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"
    - "dense associative memory is robust to adversarial inputs"
    - "conducting credit assignment by aligning local representations"
    - "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"
    - "conducting credit assignment by aligning local 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 "a neural temporal model for human motion prediction"):
  with members:
    - "learning simpler language models with the differential state framework"
    - "learning human motion models for long term predictions"
    - "on human motion prediction using recurrent neural networks"
    - "piecewise latent variables for neural variational text processing"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.NE(Neural and Evolutionary Computing)