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: "daqn deep auto encoder and q network"
  Abstract: The deep reinforcement learning method usually requires a large number of training images and executing actions to obtain sufficient results. When it is extended a real-task in the real environment with an actual robot, the method will be required more training images due to complexities or noises of the input images, and executing a lot of actions on the real robot also becomes a serious problem. Therefore, we propose an extended deep reinforcement learning method that is applied a generative model to initialize the network for reducing the number of training trials. In this paper, we used a deep q-network method as the deep reinforcement learning method and a deep auto-encoder as the generative model. We conducted experiments on three different tasks: a cart-pole game, an atari game, and a real-game with an actual robot. The proposed method trained efficiently on all tasks than the previous method, especially 2.5 times faster on a task with real environment images.

Connected through hyperedge HE-1 (induced by source paper "jointly pre training with supervised autoencoder and value losses for deep reinforcement learning"):
  with members:
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "self imitation learning"
    - "observe and look further achieving consistent performance on atari"
    - "kickstarting deep reinforcement learning"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "deep reinforcement learning an overview"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "spatially weighted anomaly detection with regression model"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "spatially weighted anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "exploiting the potential of deep reinforcement learning for classification tasks in high dimensional and unstructured data"):
  with members:
    - "classification with costly features using deep reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "datum wise classification a sequential approach to sparsity"

Connected through hyperedge HE-4 (induced by source paper "internal model from observations for reward shaping"):
  with members:
    - "behavioral cloning from observation"
    - "robust imitation of diverse behaviors"
    - "curiosity driven exploration by self supervised prediction"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "asynchronous methods for deep reinforcement learning"
    - "trust region policy optimization"

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.CV(Computer Vision and Pattern Recognition)