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 "a neurobiological evaluation metric for neural network model search"):
  Abstract: Neuroscience theory posits that the brain's visual system coarsely identifies broad object categories via neural activation patterns, with similar objects producing similar neural responses. Artificial neural networks also have internal activation behavior in response to stimuli. We hypothesize that networks exhibiting brain-like activation behavior will demonstrate brain-like characteristics, e.g., stronger generalization capabilities. In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior. To calculate HMS, representational dissimilarity matrices (RDMs) are created as abstractions of activation behavior, measured by the correlations of activations to stimulus pairs. HMS is then the correlation between the fMRI RDM and the neural network RDM across all stimulus pairs. We test the metric on unsupervised predictive coding networks, which specifically model visual perception, and assess the metric for statistical significance over a large range of hyperparameters. Our experiments show that networks with increased human-model similarity are correlated with better performance on two computer vision tasks: next frame prediction and object matching accuracy. Further, HMS identifies networks with high performance on both tasks. An unexpected secondary finding is that the metric can be employed during training as an early-stopping mechanism.

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 "measuring human perception to improve handwritten document transcription")
  Member "totally looks like how humans compare compared to machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high level perceptual similarity is enabled by learning diverse tasks")
  Member "monas multi objective neural architecture search using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi objective neural architecture search via non stationary policy gradient")
  Member "psychlab a psychology laboratory for deep reinforcement learning agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neuroevolution of self interpretable agents")
  Member "generalisation in humans and deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods")
  Member "teacher guided architecture search":
    also connected through other hyperedges:
      (none)
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning curiosity algorithms")

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)