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: "training recurrent networks online without backtracking"
  Abstract: We introduce the "NoBackTrack" algorithm to train the parameters of dynamical systems such as recurrent neural networks. This algorithm works in an online, memoryless setting, thus requiring no backpropagation through time, and is scalable, avoiding the large computational and memory cost of maintaining the full gradient of the current state with respect to the parameters. The algorithm essentially maintains, at each time, a single search direction in parameter space. The evolution of this search direction is partly stochastic and is constructed in such a way to provide, at every time, an unbiased random estimate of the gradient of the loss function with respect to the parameters. Because the gradient estimate is unbiased, on average over time the parameter is updated as it should. The resulting gradient estimate can then be fed to a lightweight Kalman-like filter to yield an improved algorithm. For recurrent neural networks, the resulting algorithms scale linearly with the number of parameters. Preliminary tests on a simple task show that the stochastic approximation of the gradient introduced in the algorithm does not seem to introduce too much noise in the trajectory, compared to maintaining the full gradient, and confirm the good performance and scalability of the Kalman-like version of NoBackTrack.

Connected through hyperedge HE-1 (induced by source paper "the power of linear recurrent neural networks"):
  with members:
    - "analysing soccer games with clustering and conceptors"
    - "difference between memory and prediction in linear recurrent networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "controlling recurrent neural networks by conceptors"
    - "learning input and recurrent weight matrices in echo state networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the variance of unbiased online recurrent optimization"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "approximating real time recurrent learning with random kronecker factors"
    - "reversible recurrent neural networks"
    - "rudder return decomposition for delayed rewards"
    - "the reversible residual network backpropagation without storing activations"
    - "unbiased online recurrent optimization"
    - "training deep nets with sublinear memory cost"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-3 (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 to adapt by minimizing discrepancy"
    - "learning simpler language models with the differential state framework"

Connected through hyperedge HE-4 (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"
    - "unbiasing truncated backpropagation through time"
    - "evolved policy gradients"
    - "learning gradient descent better generalization and longer horizons"

Connected through hyperedge HE-5 (induced by source paper "sparse attentive backtracking long range credit assignment in recurrent networks"):
  with members:
    - "unbiased online recurrent optimization"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "samplernn an unconditional end to end neural audio generation model"
    - "densely connected convolutional networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-6 (induced by source paper "sparse attentive backtracking temporal creditassignment through reminding"):
  with members:
    - "attention is all you need"
    - "towards a biologically plausible backprop"
    - "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"
    - "unitary evolution recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "approximating real time recurrent learning with random kronecker factors"):
  with members:
    - "unbiasing truncated backpropagation through time"
    - "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"
    - "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 "predictive neural networks"):
  with members:
    - "analysing soccer games with clustering and conceptors"
    - "robocupsimdata a robocup soccer research dataset"
    - "difference between memory and prediction in linear recurrent networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "controlling recurrent neural networks by conceptors"

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)