human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "talakat bullet hell generation through constrained map elites":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mech elites illuminating the mechanic space of gvgai")
  Member "real world evolution adapts robot morphology and control to hardware limitations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data efficient learning of morphology and controller for a microrobot")
  Member "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective reinforcement learning through evolutionary surrogate assisted prescription")
  Member "hierarchical behavioral repertoires with unsupervised descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised learning and exploration of reachable outcome space")
  Member "unsupervised learning of goal spaces for intrinsically motivated goal exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "off policy deep reinforcement learning with analogous disentangled exploration")
  Member "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "design validation and case studies of 2d vsr sim an optimization friendly simulator of 2 d voxel based soft robots")
  Member "quality and diversity optimization a unifying modular framework":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling map elites to deep neuroevolution")
  Member "robust lstm autoencoders for face de occlusion in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "does generative face completion help face recognition")

Question: Please tell me which class the source paper 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)