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: "deep recurrent q learning for partially observable mdps"
  Abstract: Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting \textit{Deep Recurrent Q-Network} (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens. Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN's performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN's performance degrades less than DQN's. Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN's input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "distilling policy distillation"
    - "sequential triggers for watermarking of deep reinforcement learning policies"
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "adversarial exploitation of policy imitation"
    - "wasserstein robust reinforcement learning"
    - "rl based method for benchmarking the adversarial resilience and robustness of deep reinforcement learning policies"
    - "analysis and improvement of adversarial training in dqn agents with adversarially guided exploration age"

Connected through hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "large scale study of curiosity driven learning"
    - "accelerated methods for deep reinforcement learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-4 (induced by source paper "a visual communication map for multi agent deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "adversarial advantage actor critic model for task completion dialogue policy learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "jointly learning to recommend and advertise"):
  with members:
    - "solving continual combinatorial selection via deep reinforcement learning"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "reinforcement learning based recommender system using biclustering technique"

Connected through hyperedge HE-6 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning"):
  with members:
    - "effective medical test suggestions using deep reinforcement learning"
    - "classification with costly features as a sequential decision making problem"
    - "discretizing continuous action space for on policy optimization"
    - "graph neural networks a review of methods and applications"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"

Connected through hyperedge HE-7 (induced by source paper "the chef s hat simulation environment for reinforcement learning based agents"):
  with members:
    - "it s food fight introducing the chef s hat card game for affective aware hri"
    - "deep reinforcement learning for general video game ai"
    - "qualitative measurements of policy discrepancy for return based deep q network"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"
    - "extending the openai gym for robotics a toolkit for reinforcement learning using ros and gazebo"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "evolution of a complex predator prey ecosystem on large scale multi agent deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "an empirical study of ai population dynamics with million agent reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "playing atari with deep reinforcement 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)