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: "variance reduction for reinforcement learning in input driven environments"
  Abstract: We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns. Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training. We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines. We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs. Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

Connected through hyperedge HE-1 (induced by source paper "policy optimization with stochastic mirror descent"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "trajectory wise control variates for variance reduction in policy gradient methods"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "predictor corrector policy optimization"
    - "stochastic variance reduced policy gradient"

Connected through hyperedge HE-2 (induced by source paper "comyco quality aware adaptive video streaming via imitation learning"):
  with members:
    - "guided meta policy search"
    - "an algorithmic perspective on imitation learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "tiyuntsong a self play reinforcement learning approach for abr video streaming"
    - "cross layer effects on training neural algorithms for video streaming"
    - "dart noise injection for robust imitation learning"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-3 (induced by source paper "learning in situ a randomized experiment in video streaming"):
  with members:
    - "challenges of real world reinforcement learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "bola near optimal bitrate adaptation for online videos"
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "learning compact models for planning with exogenous processes"):
  with members:
    - "discovering and removing exogenous state variables and rewards for reinforcement learning"
    - "mine mutual information neural estimation"
    - "contingency aware exploration in reinforcement learning"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-5 (induced by source paper "learning scheduling algorithms for data processing clusters"):
  with members:
    - "neo a learned query optimizer"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "relational inductive biases deep learning and graph networks"
    - "model based reinforcement learning via meta policy optimization"
    - "robust adversarial reinforcement learning"
    - "rl 2 fast reinforcement learning via slow 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)