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: "adaapt a deep architecture for adaptive policy transfer from multiple sources"
  Abstract: The ability to transfer knowledge from learnt source tasks to a new target task can be very useful in speeding up the learning process of a Reinforcement Learning agent. This has been receiving a lot of attention, but the application of transfer poses two serious challenges which have not been adequately addressed in the past. First, the agent should be able to avoid negative transfer, which happens when the transfer hampers or slows down the learning instead of speeding it up. Secondly, the agent should be able to do selective transfer which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task. We propose ADAAPT: A Deep Architecture for Adaptive Policy Transfer, which addresses these challenges. We test ADAAPT using two different instantiations: One as ADAAPTive REINFORCE algorithm for direct policy search and another as ADAAPTive Actor-Critic where the actor uses ADAAPT. Empirical evaluations on simulated domains show that ADAAPT can be effectively used for policy transfer from multiple source MDPs sharing the same state and action space.

Connected through hyperedge HE-1 (induced by source paper "attentive multi task deep reinforcement learning"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "successor features support model based and model free reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "playing hard exploration games by watching youtube"
    - "observe and look further achieving consistent performance on atari"
    - "kickstarting deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "option encoder a framework for discovering a policy basis in reinforcement learning"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "feudal networks for hierarchical reinforcement learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "proximal policy optimization algorithms"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "the option critic architecture"
    - "attend adapt and transfer attentive deep architecture for adaptive transfer from multiple sources in the same domain"

Connected through hyperedge HE-3 (induced by source paper "scaling simulation to real transfer by learning composable robot skills"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "meta reinforcement learning of structured exploration strategies"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "progressive neural networks"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "progressive reinforcement learning with distillation for multi skilled motion control"):
  with members:
    - "continual learning with deep generative replay"
    - "learning human behaviors from motion capture by adversarial imitation"
    - "on human motion prediction using recurrent neural networks"
    - "distral robust multitask reinforcement learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning and transfer of modulated locomotor controllers"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "learning without forgetting"

Connected through hyperedge HE-5 (induced by source paper "collaborative deep reinforcement learning"):
  with members:
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "playing fps games with deep reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "simultaneous deep transfer across domains and tasks"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "mazebase a sandbox for learning from games"):
  with members:
    - "a roadmap towards machine intelligence"
    - "learning simple algorithms from examples"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural turing machines"
    - "the arcade learning environment an evaluation platform for general agents"
    - "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.AI(Artificial Intelligence)