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: "scalable co optimization of morphology and control in embodied machines"
  Abstract: Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge -- as evidenced by the little improvement upon early techniques over the decades since their introduction. Embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. This treatment tends to yield individuals that are significantly more fit than those that existed before the morphological change and increases evolvability. We also show the potential for this method to avoid local optima and show fitness increases further into optimization, as well as the potential for convergence to similar highly fit morphologies across widely varying initial conditions. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training.

Connected through hyperedge HE-1 (induced by source paper "improving deep neuroevolution via deep innovation protection"):
  with members:
    - "weight agnostic neural networks"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "safe mutations for deep and recurrent neural networks through output gradients"

Connected through hyperedge HE-2 (induced by source paper "learning directed locomotion in modular robots with evolvable morphologies"):
  with members:
    - "evolving embodied intelligence from materials to machines"
    - "data efficient learning of morphology and controller for a microrobot"
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "robots that can adapt like animals"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "embodiment dictates learnability in neural controllers"):
  with members:
    - "automated shapeshifting for function recovery in damaged robots"
    - "combating catastrophic forgetting with developmental compression"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-4 (induced by source paper "sim2real gap is non monotonic with robot complexity for morphology in the loop flapping wing design"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "towards the targeted environment specific evolution of robot components"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"

Connected through hyperedge HE-5 (induced by source paper "robot co design beyond the monotone case"):
  with members:
    - "a mathematical theory of co design"

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.AI(Artificial Intelligence)