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: "autonomous skill discovery with quality diversity and unsupervised descriptors"
  Abstract: Quality-Diversity optimization is a new family of optimization algorithms that, instead of searching for a single optimal solution to solving a task, searches for a large collection of solutions that all solve the task in a different way. This approach is particularly promising for learning behavioral repertoires in robotics, as such a diversity of behaviors enables robots to be more versatile and resilient. However, these algorithms require the user to manually define behavioral descriptors, which is used to determine whether two solutions are different or similar. The choice of a behavioral descriptor is crucial, as it completely changes the solution types that the algorithm derives. In this paper, we introduce a new method to automatically define this descriptor by combining Quality-Diversity algorithms with unsupervised dimensionality reduction algorithms. This approach enables robots to autonomously discover the range of their capabilities while interacting with their environment. The results from two experimental scenarios demonstrate that robot can autonomously discover a large range of possible behaviors, without any prior knowledge about their morphology and environment. Furthermore, these behaviors are deemed to be similar to hand-crafted solutions that uses domain knowledge and significantly more diverse than when using existing unsupervised methods.

Connected through hyperedge HE-1 (induced by source paper "automating representation discovery with map elites"):
  with members:
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "deep interactive evolution"
    - "isolating sources of disentanglement in variational autoencoders"
    - "evolving deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "procedural content generation through quality diversity"
    - "alphastar an evolutionary computation perspective"
    - "evaluating map elites on constrained optimization problems"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning and exploration of reachable outcome space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "from exploration to control learning object manipulation skills through novelty search and local adaptation"
    - "reset free trial and error learning for robot damage recovery"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "hierarchical behavioral repertoires with unsupervised descriptors"

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.RO(Robotics)