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 "assessing architectural similarity in populations of deep neural networks"):
  Abstract: Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures over successive generations. Despite recent research showing the efficacy of multi-parent evolutionary synthesis, little has been done to directly assess architectural similarity between networks during the synthesis process for improved parent network selection. In this work, we present a preliminary study into quantifying architectural similarity via the percentage overlap of architectural clusters. Results show that networks synthesized using architectural alignment (via gene tagging) maintain higher architectural similarities within each generation, potentially restricting the search space of highly efficient network architectures.

Includes members:
  Member "mitigating architectural mismatch during the evolutionary synthesis of deep neural networks":
    also connected through other hyperedges:
      (none)
  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 "nature vs nurture the role of environmental resources in evolutionary deep intelligence")
  Member "the microsoft 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep segmental phonetic posterior grams based discovery of non categories in l2 english speech")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep learning with darwin evolutionary synthesis of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "dynamic representations toward efficient inference on deep neural networks by decision gates")
  Member "joint training of a convolutional network and a graphical model for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.CV(Computer Vision and Pattern Recognition)