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 to play with intrinsically motivated self aware agents"
  Abstract: Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to mathematically formalize these abilities using a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologically naturalistic simulated environment in which an agent can move and interact with objects it sees, we propose a "world-model" network that learns to predict the dynamic consequences of the agent's actions. Simultaneously, we train a separate explicit "self-model" that allows the agent to track the error map of its own world-model, and then uses the self-model to adversarially challenge the developing world-model. We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering. Moreover, the world-model that the agent learns supports improved performance on object dynamics prediction, detection, localization and recognition tasks. Taken together, our results are initial steps toward creating flexible autonomous agents that self-supervise in complex novel physical environments.

Connected through hyperedge HE-1 (induced by source paper "an exploration of embodied visual exploration"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "where to look next unsupervised active visual exploration on 360 input"
    - "embodied visual recognition"
    - "the replica dataset a digital replica of indoor spaces"
    - "emergence of exploratory look around behaviors through active observation completion"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"

Connected through hyperedge HE-2 (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"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"
    - "multi objective model based policy search for data efficient learning with sparse rewards"

Connected through hyperedge HE-3 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"
    - "teacher student curriculum learning"

Connected through hyperedge HE-4 (induced by source paper "world discovery models"):
  with members:
    - "neural predictive belief representations"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "observe and look further achieving consistent performance on atari"
    - "model based active exploration"
    - "learning awareness models"
    - "representation learning with contrastive predictive coding"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "amplifying the imitation effect for reinforcement learning of ucav s mission execution"):
  with members:
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "self imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "parameter space noise for exploration"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "count based exploration with neural density models"

Connected through hyperedge HE-6 (induced by source paper "an introduction to deep reinforcement learning"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "a study on overfitting in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-7 (induced by source paper "exploration by random network distillation"):
  with members:
    - "count based exploration with the successor representation"
    - "temporal difference models model free deep rl for model based control"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"
    - "expert augmented actor critic for vizdoom and montezumas revenge"
    - "large scale study of curiosity driven learning"
    - "self imitation learning"

Connected through hyperedge HE-8 (induced by source paper "flexible neural representation for physics prediction"):
  with members:
    - "probing physics knowledge using tools from developmental psychology"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "visual interaction networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"

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)