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: "sparse attentive backtracking long range credit assignment in recurrent networks"
  Abstract: A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.

Connected through hyperedge HE-1 (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"
    - "unbiasing truncated backpropagation through time"
    - "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-2 (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"
    - "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"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-3 (induced by source paper "adaptive skip intervals temporal abstraction for recurrent dynamical models"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "learning and querying fast generative models for reinforcement learning"
    - "time agnostic prediction predicting predictable video frames"
    - "self supervised visual planning with temporal skip connections"
    - "value prediction network"
    - "deep reinforcement learning a brief survey"
    - "recurrent environment simulators"
    - "learning independent causal mechanisms"

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.AI(Artificial Intelligence)