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: "kickstarting deep reinforcement learning"
  Abstract: We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments.

Connected through hyperedge HE-1 (induced by source paper "efficient deep reinforcement learning through policy transfer"):
  with members:
    - "the termination critic"
    - "deepmind control suite"
    - "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"
    - "when waiting is not an option learning options with a deliberation cost"

Connected through hyperedge HE-2 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-3 (induced by source paper "information asymmetry in kl regularized rl"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "mix match agent curricula for reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "deep mutual learning"
    - "trust pcl an off policy trust region method for continuous control"
    - "fixing a broken elbo"
    - "a unified bellman equation for causal information and value in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "off policy actor critic with shared experience replay"
    - "transformer xl attentive language models beyond a fixed length context"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "supervised policy update for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"

Connected through hyperedge HE-5 (induced by source paper "discrete and continuous action representation for practical rl in video games"):
  with members:
    - "distilling policy distillation"
    - "soft actor critic for discrete action settings"
    - "discretizing continuous action space for on policy optimization"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "managing engineering systems with large state and action spaces through deep reinforcement learning"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-6 (induced by source paper "self attentional credit assignment for transfer in reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "learning to generalize from sparse and underspecified rewards"
    - "reward shaping via meta learning"
    - "optimizing agent behavior over long time scales by transporting value"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "rudder return decomposition for delayed rewards"

Connected through hyperedge HE-7 (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"
    - "mix match agent curricula for reinforcement learning"
    - "progressive neural networks"

Connected through hyperedge HE-8 (induced by source paper "zpd teaching strategies for deep reinforcement learning from demonstrations"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "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"

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)