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: "kalman temporal differences"
  Abstract: Because reinforcement learning suffers from a lack of scalability, online value (and Q-) function approximation has received increasing interest this last decade. This contribution introduces a novel approximation scheme, namely the Kalman Temporal Differences (KTD) framework, that exhibits the following features: sample-efficiency, non-linear approximation, non-stationarity handling and uncertainty management. A first KTD-based algorithm is provided for deterministic Markov Decision Processes (MDP) which produces biased estimates in the case of stochastic transitions. Than the eXtended KTD framework (XKTD), solving stochastic MDP, is described. Convergence is analyzed for special cases for both deterministic and stochastic transitions. Related algorithms are experimented on classical benchmarks. They compare favorably to the state of the art while exhibiting the announced features.

Connected through hyperedge HE-1 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-2 (induced by source paper "trust region value optimization using kalman filtering"):
  with members:
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "a distributional perspective on reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "an overview of gradient descent optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-3 (induced by source paper "joint on line learning of a zero shot spoken semantic parser and a reinforcement learning dialogue manager"):
  with members:
    - "zero shot dialog generation with cross domain latent actions"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "end to end task completion neural dialogue systems"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "efficient estimation of word representations in vector space"
    - "zero shot learning for semantic utterance classification"

Connected through hyperedge HE-4 (induced by source paper "noisy networks for exploration"):
  with members:
    - "parameter space noise for exploration"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"
    - "bayesian recurrent neural networks"
    - "deep exploration via bootstrapped dqn"
    - "training recurrent neural networks by diffusion"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-5 (induced by source paper "a distributional perspective on reinforcement learning"):
  with members:
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "pixel recurrent neural networks"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "assumed density filtering q learning"):
  with members:
    - "efficient exploration through bayesian deep q networks"
    - "the uncertainty bellman equation and exploration"
    - "deep exploration via bootstrapped dqn"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"
    - "bayesian reinforcement learning a survey"
    - "tractable inference for complex stochastic processes"
    - "model based bayesian exploration"

Connected through hyperedge HE-7 (induced by source paper "deep robust kalman filter"):
  with members:
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "epopt learning robust neural network policies using model ensembles"
    - "an overview of gradient descent optimization algorithms"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-8 (induced by source paper "off policy learning with eligibility traces a survey"):
  with members:
    - "should one compute the temporal difference fix point or minimize the bellman residual the unified oblique projection view"

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)