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 "single image reflection removal through cascaded refinement"):
  Abstract: We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterative Boost Convolutional LSTM Network (IBCLN) that enables cascaded prediction for reflection removal. IBCLN iteratively refines estimates of the transmission and reflection layers at each step in a manner that they can boost the prediction quality for each other. The intuition is that progressive refinement of the transmission or reflection layer is aided by increasingly better estimates of these quantities as input, and that transmission and reflection are complementary to each other in a single image and thus provide helpful auxiliary information for each other's prediction. To facilitate training over multiple cascade steps, we employ LSTM to address the vanishing gradient problem, and incorporate a reconstruction loss as further training guidance at each step. In addition, we create a dataset of real-world images with reflection and ground-truth transmission layers to mitigate the problem of insufficient data. Through comprehensive experiments, IBCLN demonstrates performance that surpasses state-of-the-art reflection removal methods.

Includes members:
  Member "single image reflection removal exploiting misaligned training data and network enhancements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "generative single image reflection separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements")
  Member "single image reflection removal using deep encoder decoder network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "controllable attention for structured layered video decomposition")
  Member "single image reflection separation with perceptual losses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "crrn multi scale guided concurrent reflection removal network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements")
  Member "attentive generative adversarial network for raindrop removal from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vcnet a robust approach to blind image inpainting")
  Member "reflection separation using guided annotation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reflection prior")
  Member "a generic deep architecture for single image reflection removal and image smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")

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)