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: "curious intrinsically motivated modular multi goal reinforcement learning"
  Abstract: In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties.

Connected through hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "emergent tool use from multi agent autocurricula"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"

Connected through hyperedge HE-2 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "skew fit state covering self supervised reinforcement learning"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "monet unsupervised scene decomposition and representation"
    - "extending machine language models toward human level language understanding"
    - "using natural language for reward shaping in reinforcement learning"
    - "on the limitations of representing functions on sets"
    - "from language to goals inverse reinforcement learning for vision based instruction following"

Connected through hyperedge HE-3 (induced by source paper "adversarial active exploration for inverse dynamics model learning"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "learning how to active learn a deep reinforcement learning approach"
    - "robust adversarial reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"

Connected through hyperedge HE-4 (induced by source paper "intrinsic motivation and episodic memories for robot exploration of high dimensional sensory spaces"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration via state marginal matching"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "robel robotics benchmarks for learning with low cost robots"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "concurrent meta reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "provably efficient maximum entropy exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "exploration by random network distillation"

Connected through hyperedge HE-6 (induced by source paper "exploration via hindsight goal generation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "maximum entropy regularized multi goal reinforcement learning"
    - "addressing sample complexity in visual tasks using her and hallucinatory gans"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "curiosity driven multi criteria hindsight experience replay"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "exploration by random network distillation"
    - "improvements on hindsight learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"

Connected through hyperedge HE-8 (induced by source paper "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"):
  with members:
    - "s trigger continual state representation learning via self triggered generative replay"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "marginal replay vs conditional replay for continual 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.AI(Artificial Intelligence)