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: "hierarchical behavioral repertoires with unsupervised descriptors"
  Abstract: Enabling artificial agents to automatically learn complex, versatile and high-performing behaviors is a long-lasting challenge. This paper presents a step in this direction with hierarchical behavioral repertoires that stack several behavioral repertoires to generate sophisticated behaviors. Each repertoire of this architecture uses the lower repertoires to create complex behaviors as sequences of simpler ones, while only the lowest repertoire directly controls the agent's movements. This paper also introduces a novel approach to automatically define behavioral descriptors thanks to an unsupervised neural network that organizes the produced high-level behaviors. The experiments show that the proposed architecture enables a robot to learn how to draw digits in an unsupervised manner after having learned to draw lines and arcs. Compared to traditional behavioral repertoires, the proposed architecture reduces the dimensionality of the optimization problems by orders of magnitude and provides behaviors with a twice better fitness. More importantly, it enables the transfer of knowledge between robots: a hierarchical repertoire evolved for a robotic arm to draw digits can be transferred to a humanoid robot by simply changing the lowest layer of the hierarchy. This enables the humanoid to draw digits although it has never been trained for this task.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning and exploration of reachable outcome space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "autonomous skill discovery with quality diversity and unsupervised descriptors"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "real world evolution adapts robot morphology and control to hardware limitations"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"
    - "quality and diversity optimization a unifying modular framework"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "robots that can adapt like animals"

Connected through hyperedge HE-3 (induced by source paper "alphastar an evolutionary computation perspective"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "re evaluating evaluation"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "quality and diversity optimization a unifying modular framework"
    - "competitive coevolution through evolutionary complexification"

Connected through hyperedge HE-4 (induced by source paper "evolving embodied intelligence from materials to machines"):
  with members:
    - "interoceptive robustness through environment mediated morphological development"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "using parameterized black box priors to scale up model based policy search for robotics"
    - "black box data efficient policy search for robotics"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "evolutionary approaches to expensive optimisation"

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)