human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "evaluating and improving the correspondence between deep neural networks and human representations"
  Abstract: Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations. We find that state-of-the-art object classification networks provide surprisingly accurate predictions of human similarity judgments for natural images, but fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets.

Connected through hyperedge HE-1 (induced by source paper "pragmatic inference and visual abstraction enable contextual flexibility during visual communication"):
  with members:
    - "flexible neural representation for physics prediction"
    - "pragmatically informative image captioning with character level reference"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "searching for activation functions"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "a neural representation of sketch drawings"
    - "interaction networks for learning about objects relations and physics"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-2 (induced by source paper "scaling up psychology via scientific regret minimization a case study in moral decision making"):
  with members:
    - "using machine learning to guide cognitive modeling a case study in moral reasoning"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "a computational model of commonsense moral decision making"
    - "on calibration of modern neural networks"
    - "the theory is predictive but is it complete an application to human perception of randomness"
    - "how deep is knowledge tracing"

Connected through hyperedge HE-3 (induced by source paper "capturing human categorization of natural images at scale by combining deep networks and cognitive models"):
  with members:
    - "densely connected convolutional networks"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "generalizing psychological similarity spaces to unseen stimuli"):
  with members:
    - "mapping images to psychological similarity spaces using neural networks"
    - "rethinking the inception architecture for computer vision"

Question: Please tell me which class the center node 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)