human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nature vs nurture the role of environmental resources in evolutionary deep intelligence"):
  Abstract: Evolutionary deep intelligence synthesizes highly efficient deep neural networks architectures over successive generations. Inspired by the nature versus nurture debate, we propose a study to examine the role of external factors on the network synthesis process by varying the availability of simulated environmental resources. Experimental results were obtained for networks synthesized via asexual evolutionary synthesis (1-parent) and sexual evolutionary synthesis (2-parent, 3-parent, and 5-parent) using a 10% subset of the MNIST dataset. Results show that a lower environmental factor model resulted in a more gradual loss in performance accuracy and decrease in storage size. This potentially allows significantly reduced storage size with minimal to no drop in performance accuracy, and the best networks were synthesized using the lowest environmental factor models.

Includes members:
  Member "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assessing architectural similarity in populations of deep neural networks")
  Member "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evolutionary construction of convolutional neural networks")
  Member "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mitigating architectural mismatch during the evolutionary synthesis of deep neural networks")
  Member "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from handcrafted to deep learning based cancer radiomics challenges and opportunities")
  Member "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design")
  Member "deep learning with darwin evolutionary synthesis of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing convolution through the eyes of finite transformation semigroup theory an abstract algebraic interpretation of convolutional neural networks")
  Member "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "assessing architectural similarity in populations of deep neural networks")
  Member "deep compression compressing deep neural networks with pruning trained quantization and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)