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: "unbiased online recurrent optimization"
  Abstract: The novel Unbiased Online Recurrent Optimization (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models. It works in a streaming fashion and avoids backtracking through past activations and inputs. UORO is computationally as costly as Truncated Backpropagation Through Time (truncated BPTT), a widespread algorithm for online learning of recurrent networks. UORO is a modification of NoBackTrack that bypasses the need for model sparsity and makes implementation easy in current deep learning frameworks, even for complex models. #R##N#Like NoBackTrack, UORO provides unbiased gradient estimates; unbiasedness is the core hypothesis in stochastic gradient descent theory, without which convergence to a local optimum is not guaranteed. On the contrary, truncated BPTT does not provide this property, leading to possible divergence. #R##N#On synthetic tasks where truncated BPTT is shown to diverge, UORO converges. For instance, when a parameter has a positive short-term but negative long-term influence, truncated BPTT diverges unless the truncation span is very significantly longer than the intrinsic temporal range of the interactions, while UORO performs well thanks to the unbiasedness of its gradients.

Connected through hyperedge HE-1 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "approximating real time recurrent learning with random kronecker factors"
    - "reviving and improving recurrent back propagation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "reversible recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"

Connected through hyperedge HE-2 (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"
    - "unbiasing truncated backpropagation through time"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "decoupling hierarchical recurrent neural networks with locally computable losses"):
  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"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "asynchronous methods for deep reinforcement learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "training deep nets with sublinear memory cost"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-4 (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"
    - "biologically motivated algorithms for propagating local target representations"
    - "evaluating the ability of lstms to learn context free grammars"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-5 (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"
    - "training deep nets with sublinear memory cost"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "training recurrent networks online without backtracking"

Connected through hyperedge HE-6 (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"
    - "learning to adapt by minimizing discrepancy"
    - "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-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"
    - "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-8 (induced by source paper "low pass recurrent neural networks a memory architecture for longer term correlation discovery"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the statistical recurrent unit"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "neural episodic control"
    - "scale invariant temporal history sith optimal slicing of the past in an uncertain world"
    - "population based training of neural networks"
    - "associative long short term memory"
    - "reinforcement learning with unsupervised auxiliary tasks"

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)