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 temporal creditassignment through reminding"
  Abstract: Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention.

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"
    - "unbiasing truncated backpropagation through time"
    - "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 "armin towards a more efficient and light weight recurrent memory network"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "towards binary valued gates for robust lstm training"
    - "fast slow recurrent neural networks"
    - "memory augmented neural networks with wormhole connections"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "associative long short term memory"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-3 (induced by source paper "generalization of reinforcement learners with working and episodic memory"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational deep reinforcement learning"
    - "neural predictive belief representations"
    - "fast deep reinforcement learning using online adjustments from the past"
    - "relational recurrent neural networks"
    - "quantifying generalization in reinforcement learning"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"

Connected through hyperedge HE-4 (induced by source paper "understanding and controlling memory in recurrent neural networks"):
  with members:
    - "identifying and controlling important neurons in neural machine translation"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "visualizing and understanding curriculum learning for long short term memory networks"
    - "control of memory active perception and action in minecraft"
    - "end to end memory networks"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-5 (induced by source paper "rudder return decomposition for delayed rewards"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "meta gradient reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "world models"
    - "methods for interpreting and understanding deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "optimizing agent behavior over long time scales by transporting value"):
  with members:
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "rudder return decomposition for delayed rewards"
    - "recurrent world models facilitate policy evolution"
    - "asynchronous methods for deep reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)