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: "automatic goal generation for reinforcement learning agents"
  Abstract: Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing. We use a generator network to propose tasks for the agent to try to achieve, specified as goal states. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment. Our method can also learn to achieve tasks with sparse rewards, which traditionally pose significant challenges.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-2 (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"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "semi supervised class discovery"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "continual unsupervised representation learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "revisiting self supervised visual representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "co teaching robust training of deep neural networks with extremely noisy labels"

Connected through hyperedge HE-4 (induced by source paper "enhanced poet open ended reinforcement learning through unbounded invention of learning challenges and their solutions"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "go explore a new approach for hard exploration problems"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "alphastar an evolutionary computation perspective"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "teacher student curriculum learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "soft hindsight experience replay"):
  with members:
    - "maximum entropy regularized multi goal reinforcement learning"
    - "alphastar an evolutionary computation perspective"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "if maxent rl is the answer what is the question"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-6 (induced by source paper "deep adversarial reinforcement learning for object disentangling"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dota 2 with large scale deep reinforcement learning"
    - "risk averse robust adversarial reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "adversarial attacks on neural network policies"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-7 (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"
    - "learning curriculum policies for reinforcement learning"
    - "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"
    - "teacher student curriculum learning"

Connected through hyperedge HE-8 (induced by source paper "glib exploration via goal literal babbling for lifted operator learning"):
  with members:
    - "playgol learning programs through play"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "intrinsically motivated goal exploration processes with automatic curriculum 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)