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: "attend adapt and transfer attentive deep architecture for adaptive transfer from multiple sources in the same domain"
  Abstract: Transferring knowledge from prior source tasks in solving a new target task can be useful in several learning applications. The application of transfer poses two serious challenges which have not been adequately addressed. First, the agent should be able to avoid negative transfer, which happens when the transfer hampers or slows down the learning instead of helping it. Second, the agent should be able to selectively 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 A2T (Attend, Adapt and Transfer), an attentive deep architecture which adapts and transfers from these source tasks. Our model is generic enough to effect transfer of either policies or value functions. Empirical evaluations on different learning algorithms show that A2T is an effective architecture for transfer by being able to avoid negative transfer while transferring selectively from multiple source tasks in the same domain.

Connected through hyperedge HE-1 (induced by source paper "efficient deep reinforcement learning through policy transfer"):
  with members:
    - "the termination critic"
    - "deepmind control suite"
    - "kickstarting deep reinforcement learning"
    - "context aware policy reuse"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "learning to compose skills"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "learnings options end to end for continuous action tasks"

Connected through hyperedge HE-2 (induced by source paper "contextual policy reuse using deep mixture models"):
  with members:
    - "model based reinforcement learning for atari"
    - "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"
    - "context aware policy reuse"
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-3 (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-4 (induced by source paper "transfer learning by modeling a distribution over policies"):
  with members:
    - "safe option critic learning safety in the option critic architecture"
    - "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"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "vfunc a deep generative model for functions"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"

Connected through hyperedge HE-5 (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"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "transfer in deep reinforcement learning using knowledge graphs"):
  with members:
    - "towards solving text based games by producing adaptive action spaces"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "progressive neural networks"
    - "what can you do with a rock affordance extraction via word embeddings"
    - "deep reinforcement learning with a natural language action space"
    - "actor mimic deep multitask and transfer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "transfer of deep reactive policies for mdp planning"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "teacher student curriculum learning"
    - "learning generalized reactive policies using deep neural networks"
    - "towards deep symbolic reinforcement learning"
    - "value iteration networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "a theory of goal oriented mdps with dead ends"

Connected through hyperedge HE-8 (induced by source paper "shared learning enhancing reinforcement in q ensembles"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "building machines that learn and think like people"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "online transfer learning in reinforcement learning domains"
    - "prioritized experience replay"

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)