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 "quantifying legibility of indoor spaces using deep convolutional neural networks case studies in train stations"):
  Abstract: Abstract   Legibility is the extent to which space can be easily recognized. Evaluating legibility is particularly desirable in indoor spaces, since it has a large impact on human behavior and the efficiency of space utilization. However, indoor space legibility has only been studied through survey and trivial simulations and lacks scalable quantitative measurement. We utilized a Deep Convolutional Neural Network (DCNN), which is structurally similar to a human perception system, to model legibility in indoor spaces. To implement the modelling of legibility for any indoor space, we designed an end-to-end processing pipeline from indoor data retrieving to model training to spatial legibility analysis. Although the model performed very well (98% accuracy) overall, there are still discrepancies in model's recognizing confidence among different spaces, reflecting legibility differences. To prove the validity of the pipeline, we deployed a survey on Amazon Mechanical Turk, collecting 4015 samples. Meanwhile, we also conducted an identical survey, collecting 570 samples, on occupants in the station. The human samples showed a similar behavior pattern and mechanism as the DCNN models. Further, we used model results to visually explain legibility differences resulting from architectural program, building age, building style, as well as identify visual clusterings of spaces.

Includes members:
  Member "comparing deep neural networks against humans object recognition when the signal gets weaker":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomaly detection in video data based on probabilistic latent space models")
  Member "r2cnn rotational region cnn for orientation robust scene text detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "deep learning the city quantifying urban perception at a global scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes")
  Member "deep networks can resemble human feed forward vision in invariant object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning in spiking neural networks")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "learning deep features for discriminative localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "object detectors emerge in deep scene cnns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")

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.CY(Computers and Society)