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: "koniq 10k towards an ecologically valid and large scale iqa database"
  Abstract: The main challenge in applying state-of-the-art deep learning methods to predict image quality in-the-wild is the relatively small size of existing quality scored datasets. The reason for the lack of larger datasets is the massive resources required in generating diverse and publishable content. We present a new systematic and scalable approach to create large-scale, authentic and diverse image datasets for Image Quality Assessment (IQA). We show how we built an IQA database, KonIQ-10k, consisting of 10,073 images, on which we performed very large scale crowdsourcing experiments in order to obtain reliable quality ratings from 1,467 crowd workers (1.2 million ratings). We argue for its ecological validity by analyzing the diversity of the dataset, by comparing it to state-of-the-art IQA databases, and by checking the reliability of our user studies.

Connected through hyperedge HE-1 (induced by source paper "from patches to pictures paq 2 piq mapping the perceptual space of picture quality"):
  with members:
    - "learning to blindly assess image quality in the laboratory and wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "nima neural image assessment"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "on the use of deep learning for blind image quality assessment"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-2 (induced by source paper "learning to blindly assess image quality in the laboratory and wild"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "perceptually optimized image rendering"
    - "massive online crowdsourced study of subjective and objective picture quality"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "algorithm selection for image quality assessment"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "aslib a benchmark library for algorithm selection"

Connected through hyperedge HE-4 (induced by source paper "blind predicting similar quality map for image quality assessment"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "focal loss for dense object detection"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "mask r cnn"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"
    - "feature pyramid networks for object detection"

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)