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: "real world evolution adapts robot morphology and control to hardware limitations"
  Abstract: For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this real-world evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.

Connected through hyperedge HE-1 (induced by source paper "autonomous skill discovery with quality diversity and unsupervised descriptors"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "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-2 (induced by source paper "data efficient learning of morphology and controller for a microrobot"):
  with members:
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "from the lab to the desert fast prototyping and learning of robot locomotion"
    - "funneled bayesian optimization for design tuning and control of autonomous systems"
    - "the cma evolution strategy a tutorial"
    - "robots that can adapt like animals"

Connected through hyperedge HE-3 (induced by source paper "lessons learned from real world experiments with dyret the dynamic robot for embodied testing"):
  with members:
    - "evolving robots on easy mode towards a variable complexity controller for quadrupeds"
    - "evolved embodied phase coordination enables robust quadruped robot locomotion"
    - "exploring mechanically self reconfiguring robots for autonomous design"
    - "self modifying morphology experiments with dyret dynamic robot for embodied testing"

Connected through hyperedge HE-4 (induced by source paper "data efficient co adaptation of morphology and behaviour with deep reinforcement learning"):
  with members:
    - "reinforcement learning for improving agent design"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "jointly learning to construct and control agents using deep reinforcement learning"
    - "from the lab to the desert fast prototyping and learning of robot locomotion"

Connected through hyperedge HE-5 (induced by source paper "evolving robots on easy mode towards a variable complexity controller for quadrupeds"):
  with members:
    - "self modifying morphology experiments with dyret dynamic robot for embodied testing"
    - "robots that can adapt like animals"
    - "evolution of a subsumption architecture neurocontroller"

Connected through hyperedge HE-6 (induced by source paper "evolved embodied phase coordination enables robust quadruped robot locomotion"):
  with members:
    - "self modifying morphology experiments with dyret dynamic robot for embodied testing"
    - "evolving a behavioral repertoire for a walking robot"

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)