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: "unbiasing truncated backpropagation through time"
  Abstract: \emph{Truncated Backpropagation Through Time} (truncated BPTT, \cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of \emph{Backpropagation Through Time} (BPTT \cite{werbos:bptt}) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce \emph{Anticipated Reweighted Truncated Backpropagation} (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling \cite{ptb_proc}, ARTBP slightly outperforms truncated BPTT.

Connected through hyperedge HE-1 (induced by source paper "optimal kronecker sum approximation of real time recurrent learning"):
  with members:
    - "on the variance of unbiased online recurrent optimization"
    - "approximating real time recurrent learning with random kronecker factors"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "reviving and improving recurrent back propagation"
    - "an analysis of neural language modeling at multiple scales"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-2 (induced by source paper "efficient optimization of loops and limits with randomized telescoping sums"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "learned optimizers that outperform on wall clock and validation loss"
    - "understanding short horizon bias in stochastic meta optimization"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "truncated back propagation for bilevel optimization"
    - "an analysis of neural language modeling at multiple scales"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "stochastic hyperparameter optimization through hypernetworks"

Connected through hyperedge HE-3 (induced by source paper "learning discrete structures for graph neural networks"):
  with members:
    - "link prediction based on graph neural networks"
    - "learning deep generative models of graphs"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural message passing for quantum chemistry"
    - "learning graph representations with embedding propagation"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "fma a dataset for music analysis"

Connected through hyperedge HE-4 (induced by source paper "adaptively truncating backpropagation through time to control gradient bias"):
  with members:
    - "approximating real time recurrent learning with random kronecker factors"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "stable recurrent models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "quasi recurrent neural networks"
    - "pointer sentinel mixture models"
    - "a recurrent neural network without chaos"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "character level language modeling with deeper self attention"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "improving gans using optimal transport"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "learning to generate reviews and discovering sentiment"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"

Connected through hyperedge HE-6 (induced by source paper "understanding and correcting pathologies in the training of learned optimizers"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "understanding short horizon bias in stochastic meta optimization"
    - "learning to optimize neural nets"
    - "aggregated momentum stability through passive damping"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "evolved policy gradients"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-7 (induced by source paper "approximating real time recurrent learning with random kronecker factors"):
  with members:
    - "an analysis of neural language modeling at multiple scales"
    - "on the state of the art of evaluation in neural language models"
    - "unbiased online recurrent optimization"
    - "decoupled neural interfaces using synthetic gradients"
    - "recurrent highway networks"
    - "training recurrent networks online without backtracking"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "reviving and improving recurrent back propagation"):
  with members:
    - "gated graph sequence neural networks"

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)