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: "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"
  Abstract: Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary system is put in place in order to perform a broad investigation on the free-form evolution of walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two sets explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance tradeoffs. It is found that within our simplified physics world stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land - water) during evolution. Results provide interesting morphological exaptation phenomena, and point out a potential asymmetry between land-water and water-land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.

Connected through hyperedge HE-1 (induced by source paper "design validation and case studies of 2d vsr sim an optimization friendly simulator of 2 d voxel based soft robots"):
  with members:
    - "automated shapeshifting for function recovery in damaged robots"
    - "titan a parallel asynchronous library for multi agent and soft body robotics using nvidia cuda"
    - "scalable sim to real transfer of soft robot designs"
    - "difftaichi differentiable programming for physical simulation"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning for improving agent design"):
  with members:
    - "co creative level design via machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "jointly learning to construct and control agents using deep reinforcement learning"
    - "procedural content generation via machine learning pcgml"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "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-4 (induced by source paper "comparing direct and indirect representations for environment specific robot component design"):
  with members:
    - "towards the targeted environment specific evolution of robot components"
    - "toward the coevolution of novel vertical axis wind turbines"

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)