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: "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"
  Abstract: The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.

Connected through hyperedge HE-1 (induced by source paper "is the red square big malevic modeling adjectives leveraging visual contexts"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "how clever is the film model and how clever can it be"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "systematic generalization what is required and can it be learned"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "pragmatically informative image captioning with character level reference"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-2 (induced by source paper "multimodal logical inference system for visual textual entailment"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "visual entailment task for visually grounded language learning"
    - "tallyqa answering complex counting questions"
    - "grounded textual entailment"
    - "attentive relational networks for mapping images to scene graphs"
    - "scene graph generation by iterative message passing"
    - "graph structured representations for visual question answering"
    - "vqa visual question answering"

Connected through hyperedge HE-3 (induced by source paper "the meaning of most for visual question answering models"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "a dataset and architecture for visual reasoning with a working memory"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "film visual reasoning with a general conditioning layer"
    - "analyzing the behavior of visual question answering models"

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)