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: "investigating recurrence and eligibility traces in deep q networks"
  Abstract: Eligibility traces in reinforcement learning are used as a bias-variance trade-off and can often speed up training time by propagating knowledge back over time-steps in a single update. We investigate the use of eligibility traces in combination with recurrent networks in the Atari domain.  We illustrate the benefits of both recurrent nets and eligibility traces in some Atari games, and highlight also the importance of the optimization used in the training.

Connected through hyperedge HE-1 (induced by source paper "efficient eligibility traces for deep reinforcement learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "reconciling lambda returns with experience replay"):
  with members:
    - "discrete sequential prediction of continuous actions for deep rl"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "emergence of locomotion behaviours in rich environments"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "continuous deep q learning with model based acceleration"
    - "safe and efficient off policy reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "playing fps games with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "recurrent deterministic policy gradient method for bipedal locomotion on rough terrain challenge"):
  with members:
    - "parameter space noise for exploration"
    - "a deeper look at experience replay"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-4 (induced by source paper "value decomposition networks for cooperative multi agent learning"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "adam a method for stochastic optimization"
    - "the complexity of decentralized control of markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"

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)