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: "a minimal developmental model can increase evolvability in soft robots"
  Abstract: Different subsystems of organisms adapt over many time scales, such as rapid changes in the nervous system (learning), slower morphological and neurological change over the lifetime of the organism (postnatal development), and change over many generations (evolution). Much work has focused on instantiating learning or evolution in robots, but relatively little on development. Although many theories have been forwarded as to how development can aid evolution, it is difficult to isolate each such proposed mechanism. Thus, here we introduce a minimal yet embodied model of development: the body of the robot changes over its lifetime, yet growth is not influenced by the environment. We show that even this simple developmental model confers evolvability because it allows evolution to sweep over a larger range of body plans than an equivalent non-developmental system, and subsequent heterochronic mutations 'lock in' this body plan in more morphologically-static descendants. Future work will involve gradually complexifying the developmental model to determine when and how such added complexity increases evolvability.

Connected through hyperedge HE-1 (induced by source paper "automated shapeshifting for function recovery in damaged robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "interoceptive robustness through environment mediated morphological development"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "robots that can adapt like animals"
    - "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"

Connected through hyperedge HE-2 (induced by source paper "combating catastrophic forgetting with developmental compression"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "progressive neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "dynamic routing between capsules"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "self adaptation of mutation rates in non elitist populations"

Connected through hyperedge HE-3 (induced by source paper "interoceptive robustness through environment mediated morphological development"):
  with members:
    - "evolving deep neural networks"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "synthesizing robust adversarial examples"
    - "neural architecture search with reinforcement learning"
    - "robots that can adapt like animals"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "intriguing properties of neural networks"

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.NE(Neural and Evolutionary Computing)