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: "natural gradient deep q learning"
  Abstract: This paper presents findings for training a Q-learning reinforcement learning agent using natural gradient techniques. We compare the original deep Q-network (DQN) algorithm to its natural gradient counterpart (NGDQN), measuring NGDQN and DQN performance on classic controls environments without target networks. We find that NGDQN performs favorably relative to DQN, converging to significantly better policies faster and more frequently. These results indicate that natural gradient could be used for value function optimization in reinforcement learning to accelerate and stabilize training.

Connected through hyperedge HE-1 (induced by source paper "direction concentration learning enhancing congruency in machine learning"):
  with members:
    - "learning to detect"
    - "a dilated inception network for visual saliency prediction"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "dual glance model for deciphering social relationships"
    - "gradient episodic memory for continual learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "brpo batch residual policy optimization"):
  with members:
    - "benchmarking batch deep reinforcement learning algorithms"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "residual policy learning"
    - "off policy deep reinforcement learning without exploration"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "residual reinforcement learning for robot control"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-3 (induced by source paper "towards characterizing divergence in deep q learning"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "observe and look further achieving consistent performance on atari"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "deep reinforcement learning that matters"
    - "equivalence between policy gradients and soft q learning"

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)