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 "measuring human perception to improve handwritten document transcription"):
  Abstract: The subtleties of human perception, as measured by vision scientists through the use of psychophysics, are important clues to the internal workings of visual recognition. For instance, measured reaction time can indicate whether a visual stimulus is easy for a subject to recognize, or whether it is hard. In this paper, we consider how to incorporate psychophysical measurements of visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can enforce consistency with human behavior. As a case study to assess the viability of this approach, we look at the problem of handwritten document transcription. While good progress has been made towards automatically transcribing modern handwriting, significant challenges remain in transcribing historical documents. Here we work towards a comprehensive transcription solution for Medieval manuscripts that combines networks trained using our novel loss formulation with natural language processing elements. In a baseline assessment, reliable performance is demonstrated for the standard IAM and RIMES datasets. Further, we go on to show feasibility for our approach on a previously published dataset and a new dataset of digitized Latin manuscripts, originally produced by scribes in the Cloister of St. Gall around the middle of the 9th century.

Includes members:
  Member "psyphy a psychophysics driven evaluation framework for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "visual psychophysics for making face recognition algorithms more explainable":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a neurobiological evaluation metric for neural network model search")
  Member "towards knowledge discovery from the vatican secret archives in codice ratio episode 1 machine transcription of the manuscripts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multikernel activation functions formulation and a case study")
  Member "language model supervision for handwriting recognition model adaptation":
    also connected through other hyperedges:
      (none)
  Member "comparing deep neural networks against humans object recognition when the signal gets weaker":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomaly detection in video data based on probabilistic latent space models")
  Member "using human brain activity to guide machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "white noise analysis of neural networks")
  Member "how deep is the feature analysis underlying rapid visual categorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling neural mechanisms for perceptual grouping")
  Member "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")

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)