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: "multi timescale nexting in a reinforcement learning robot"
  Abstract: The term "nexting" has been used by psychologists to refer to the propensity of people and many other animals to continually predict what will happen next in an immediate, local, and personal sense. The ability to "next" constitutes a basic kind of awareness and knowledge of one's environment. In this paper we present results with a robot that learns to next in real time, predicting thousands of features of the world's state, including all sensory inputs, at timescales from 0.1 to 8 seconds. This was achieved by treating each state feature as a reward-like target and applying temporal-difference methods to learn a corresponding value function with a discount rate corresponding to the timescale. We show that two thousand predictions, each dependent on six thousand state features, can be learned and updated online at better than 10Hz on a laptop computer, using the standard TD(lambda) algorithm with linear function approximation. We show that this approach is efficient enough to be practical, with most of the learning complete within 30 minutes. We also show that a single tile-coded feature representation suffices to accurately predict many different signals at a significant range of timescales. Finally, we show that the accuracy of our learned predictions compares favorably with the optimal off-line solution.

Connected through hyperedge HE-1 (induced by source paper "improving performance in reinforcement learning by breaking generalization in neural networks"):
  with members:
    - "meta learning representations for continual learning"
    - "towards characterizing divergence in deep q learning"
    - "the utility of sparse representations for control in reinforcement learning"
    - "a deeper look at experience replay"
    - "neural map structured memory for deep reinforcement learning"
    - "towards generalization and simplicity in continuous control"
    - "overcoming catastrophic forgetting in neural networks"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-2 (induced by source paper "what s a good prediction issues in evaluating general value functions through error"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "adapting behaviour via intrinsic reward a survey and empirical study"
    - "when is a prediction knowledge"
    - "general value function networks"
    - "a first empirical study of emphatic temporal difference learning"
    - "introspective agents confidence measures for general value functions"
    - "temporal difference networks"

Connected through hyperedge HE-3 (induced by source paper "perception as prediction using general value functions in autonomous driving applications"):
  with members:
    - "composable action conditioned predictors flexible off policy learning for robot navigation"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "learning to act by predicting the future"

Connected through hyperedge HE-4 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"

Connected through hyperedge HE-5 (induced by source paper "meta descent for online continual prediction"):
  with members:
    - "on the convergence of adam and beyond"
    - "understanding short horizon bias in stochastic meta optimization"
    - "tidbd adapting temporal difference step sizes through stochastic meta descent"
    - "online learning rate adaptation with hypergradient descent"
    - "effective sketching methods for value function approximation"
    - "learning to learn by gradient descent by gradient descent"
    - "accelerated gradient temporal difference learning"
    - "temporal difference networks"

Connected through hyperedge HE-6 (induced by source paper "gamma nets generalizing value estimation over timescale"):
  with members:
    - "separating value functions across time scales"
    - "dopamine a research framework for deep reinforcement learning"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"
    - "universal successor representations for transfer reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "learning values across many orders of magnitude"

Connected through hyperedge HE-7 (induced by source paper "importance resampling for off policy prediction"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi step off policy learning without importance sampling ratios"
    - "end to end learning for self driving cars"
    - "the predictron end to end learning and planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "importance sampling with unequal support"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "prioritized experience replay"

Connected through hyperedge HE-8 (induced by source paper "examining the use of temporal difference incremental delta bar delta for real world predictive knowledge architectures"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "general value function networks"
    - "accelerating learning in constructive predictive frameworks with the successor representation"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "communicative capital for prosthetic agents"
    - "introspective agents confidence measures for general value functions"

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)