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 "ir nas neural architecture search for image restoration"):
  Abstract: Recently, neural architecture search (NAS) methods have attracted much attention and outperformed manually designed architectures on a few high-level vision tasks. In this paper, we propose IR-NAS, an effort towards employing NAS to automatically design effective neural network architectures for low-level image restoration tasks, and apply to two such tasks: image denoising and image de-raining. IR-NAS adopts an flexible hierarchical search space, including inner cell structures and outer layer widths. The proposed IR-NAS is both memory and computationally efficient, which takes only 6 hours for searching using a single GPU and saves memory by sharing cell weights across different feature levels. We evaluate the effectiveness of our proposed IR-NAS on three different datasets, including an additive white Gaussian noise dataset BSD500, a realistic noise dataset SIM1800 and a challenging de-raining dataset Rain800. Results show that the architectures found by IR-NAS have fewer parameters and enjoy a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. We also present analysis on the architectures found by NAS.

Includes members:
  Member "generating training data for denoising real rgb images via camera pipeline simulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "memory efficient hierarchical neural architecture search for image denoising")
  Member "nas fcos fast neural architecture search for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "econas finding proxies for economical neural architecture search")
  Member "auto deeplab hierarchical neural architecture search for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "darts improved differentiable architecture search with early stopping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nas count counting by density with neural architecture search")
  Member "ffdnet toward a fast and flexible solution for cnn based image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "proxylessnas direct neural architecture search on target task and hardware":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "recurrent squeeze and excitation context aggregation net for single image deraining":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "neural nearest neighbors networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)