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: "crossing the reality gap a short introduction to the transferability approach"
  Abstract: In robotics, gradient-free optimization algorithms (e.g. evolutionary algorithms) are often used only in simulation because they require the evaluation of many candidate solutions. Nevertheless, solutions obtained in simulation often do not work well on the real device. The transferability approach aims at crossing this gap between simulation and reality by making the optimization algorithm aware of the limits of the simulation.#R##N##R##N#In the present paper, we first describe the transferability function, that maps solution descriptors to a score representing how well a simulator matches the reality. We then show that this function can be learned using a regression algorithm and a few experiments with the real devices. Our results are sup- ported by an extensive study of the reality gap for a simple quadruped robot whose control parameters are optimized. In particular, we mapped the whole search space in reality and in simulation to understand the differences between the fitness landscapes.

Connected through hyperedge HE-1 (induced by source paper "from exploration to control learning object manipulation skills through novelty search and local adaptation"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "behavioural repertoire via generative adversarial policy networks"
    - "quality and diversity optimization a unifying modular framework"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "using centroidal voronoi tessellations to scale up the multi dimensional archive of phenotypic elites algorithm"
    - "micro data learning the other end of the spectrum"

Connected through hyperedge HE-2 (induced by source paper "state representation learning for control an overview"):
  with members:
    - "decoupling dynamics and reward for transfer learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "deep reinforcement learning that matters"
    - "progressive neural networks"
    - "independently controllable factors"
    - "deep multimodal representation learning from temporal data"
    - "interactive perception leveraging action in perception and perception in action"

Connected through hyperedge HE-3 (induced by source paper "tools for dynamics simulation of robots a survey based on user feedback"):
  with members:
    - "realistic haptic rendering of interacting deformable objects in virtual environments"

Connected through hyperedge HE-4 (induced by source paper "fast damage recovery in robotics with the t resilience algorithm"):
  with members:
    - "learning to walk in every direction"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learning to walk in every direction"):
  with members:
    - "evolvability signatures of generative encodings beyond standard performance benchmarks"
    - "fast damage recovery in robotics with the t resilience algorithm"

Connected through hyperedge HE-6 (induced by source paper "evolving a behavioral repertoire for a walking robot"):
  with members:
    - "fast damage recovery in robotics with the t resilience algorithm"

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)