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: "learning to adapt by minimizing discrepancy"
  Abstract: We explore whether useful temporal neural generative models can be learned from sequential data without back-propagation through time. We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences. Specifically, we build on the concept of predictive coding, which has gained influence in cognitive science, in a neural framework. To do so we develop a novel architecture, the Temporal Neural Coding Network, and its learning algorithm, Discrepancy Reduction. The underlying directed generative model is fully recurrent, meaning that it employs structural feedback connections and temporal feedback connections, yielding information propagation cycles that create local learning signals. This facilitates a unified bottom-up and top-down approach for information transfer inside the architecture. Our proposed algorithm shows promise on the bouncing balls generative modeling problem. Further experiments could be conducted to explore the strengths and weaknesses of our approach.

Connected through hyperedge HE-1 (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-2 (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"
    - "conducting credit assignment by aligning local representations"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "overcoming catastrophic forgetting by incremental moment matching"

Connected through hyperedge HE-3 (induced by source paper "a unified framework of online learning algorithms for training recurrent neural networks"):
  with members:
    - "optimal kronecker sum approximation of real time recurrent learning"
    - "on the variance of unbiased online recurrent optimization"
    - "approximating real time recurrent learning with random kronecker factors"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "unbiased online recurrent optimization"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (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"
    - "conducting credit assignment by aligning local representations"
    - "lstm a search space odyssey"
    - "learning simpler language models with the differential state framework"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-5 (induced by source paper "conducting credit assignment by aligning local representations"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "piecewise latent variables for neural variational text processing"
    - "a modular theory of feature learning"
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders"

Connected through hyperedge HE-6 (induced by source paper "biologically motivated algorithms for propagating local target representations"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "conducting credit assignment by aligning local representations"
    - "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)