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: "learning curriculum policies for reinforcement learning"
  Abstract: Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task. Automatically choosing a sequence of such tasks (i.e. a curriculum) is an open problem that has been the subject of much recent work in this area. In this paper, we build upon a recent method for curriculum design, which formulates the curriculum sequencing problem as a Markov Decision Process. We extend this model to handle multiple transfer learning algorithms, and show for the first time that a curriculum policy over this MDP can be learned from experience. We explore various representations that make this possible, and evaluate our approach by learning curriculum policies for multiple agents in two different domains. The results show that our method produces curricula that can train agents to perform on a target task as fast or faster than existing methods.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "screenernet learning self paced curriculum for deep neural networks"
    - "theory of curriculum learning with convex loss functions"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for complex manipulation tasks with sparse feedback"):
  with members:
    - "bias reduced hindsight experience replay with virtual goal prioritization"
    - "exploration by random network distillation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"
    - "learning montezuma s revenge from a single demonstration"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"

Connected through hyperedge HE-3 (induced by source paper "adapting to unseen environments through explicit representation of context"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "procedural content generation from automatically generating game levels to increasing generality in machine learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "meta learning by the baldwin effect"
    - "automated curriculum learning by rewarding temporally rare events"
    - "lstm a search space odyssey"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "clic curriculum learning and imitation for object control in non rewarding environments"):
  with members:
    - "learning actionable representations with goal conditioned policies"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "many goals reinforcement learning"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "curious intrinsically motivated multi task multi goal reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "generative exploration and exploitation"):
  with members:
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "large scale study of curiosity driven learning"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "automated curricula through setter solver interactions"):
  with members:
    - "classification accuracy score for conditional generative models"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "visual reinforcement learning with imagined goals"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-7 (induced by source paper "from few to more large scale dynamic multiagent curriculum learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "learning transferable cooperative behavior in multi agent teams"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "factorized q learning for large scale multi agent systems"
    - "how powerful are graph neural networks"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "self paced contextual reinforcement learning"):
  with members:
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "temporal difference models model free deep rl for model based control"
    - "computational theories of curiosity driven learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "reverse curriculum generation for reinforcement learning"
    - "proximal policy optimization algorithms"

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)