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: "automated shapeshifting for function recovery in damaged robots"
  Abstract: A robot's mechanical parts routinely wear out from normal functioning and can be lost to injury. For autonomous robots operating in isolated or hostile environments, repair from a human operator is often not possible. Thus, much work has sought to automate damage recovery in robots. However, every case reported in the literature to date has accepted the damaged mechanical structure as fixed, and focused on learning new ways to control it. Here we show for the first time a robot that automatically recovers from unexpected damage by deforming its resting mechanical structure without changing its control policy. We found that, especially in the case of "deep insult", such as removal of all four of the robot's legs, the damaged machine evolves shape changes that not only recover the original level of function (locomotion) as before, but can in fact surpass the original level of performance (speed). This suggests that shape change, instead of control readaptation, may be a better method to recover function after damage in some cases.

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:
    - "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"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"

Connected through hyperedge HE-2 (induced by source paper "scalable sim to real transfer of soft robot designs"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning agile and dynamic motor skills for legged robots"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "word2vec to behavior morphology facilitates the grounding of language in machines"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "grasp2vec learning object representations from self supervised grasping"
    - "sound word2vec learning word representations grounded in sounds"
    - "morphology dictates a robot s ability to ground crowd proposed language"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "embodiment dictates learnability in neural controllers"):
  with members:
    - "combating catastrophic forgetting with developmental compression"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "scalable co optimization of morphology and control in embodied machines"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "multimodal representation models for prediction and control from partial information"):
  with members:
    - "multimodal generative models for scalable weakly supervised learning"
    - "breakingnews article annotation by image and text processing"
    - "robots that can adapt like animals"
    - "end to end training of deep visuomotor policies"

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)