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: "stabilizing off policy q learning via bootstrapping error reduction"
  Abstract: Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning. However, in practice, commonly used off-policy approximate dynamic programming methods based on Q-learning and actor-critic methods are highly sensitive to the data distribution, and can make only limited progress without collecting additional on-policy data. As a step towards more robust off-policy algorithms, we study the setting where the off-policy experience is fixed and there is no further interaction with the environment. We identify bootstrapping error as a key source of instability in current methods. Bootstrapping error is due to bootstrapping from actions that lie outside of the training data distribution, and it accumulates via the Bellman backup operator. We theoretically analyze bootstrapping error, and demonstrate how carefully constraining action selection in the backup can mitigate it. Based on our analysis, we propose a practical algorithm, bootstrapping error accumulation reduction (BEAR). We demonstrate that BEAR is able to learn robustly from different off-policy distributions, including random and suboptimal demonstrations, on a range of continuous control tasks.

Connected through hyperedge HE-1 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction"):
  with members:
    - "gradient surgery for multi task learning"
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "towards characterizing divergence in deep q learning"
    - "behavior regularized offline reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "is a good representation sufficient for sample efficient reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "keep doing what worked behavioral modelling priors for offline reinforcement learning"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "maximum a posteriori policy optimisation"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "a framework for data driven robotics"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-4 (induced by source paper "iris implicit reinforcement without interaction at scale for learning control from offline robot manipulation data"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "towards characterizing divergence in deep q learning"
    - "learning latent plans from play"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "goal conditioned imitation learning"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"

Connected through hyperedge HE-5 (induced by source paper "learning to reach goals without reinforcement learning"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "distributional reinforcement learning for efficient exploration"
    - "learning latent plans from play"
    - "relay policy learning solving long horizon tasks via imitation and reinforcement learning"
    - "goal conditioned imitation learning"
    - "temporal difference models model free deep rl for model based control"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-6 (induced by source paper "bail best action imitation learning for batch deep reinforcement learning"):
  with members:
    - "off policy evaluation via off policy classification"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-7 (induced by source paper "zpd teaching strategies for deep reinforcement learning from demonstrations"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "exploration by random network distillation"
    - "off policy deep reinforcement learning without exploration"
    - "observe and look further achieving consistent performance on atari"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-8 (induced by source paper "striving for simplicity in off policy deep reinforcement learning"):
  with members:
    - "distributional reinforcement learning with linear function approximation"
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "off policy evaluation via off policy classification"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "dopamine a research framework for deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "implicit quantile networks for distributional 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)