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: "interactive deep colorization with simultaneous global and local inputs"
  Abstract: Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it's difficult to differentiate the influence of different kind of user inputs in network training. To solve this problem, we present a novel deep colorization method, which allows simultaneous global and local inputs to better control the output colorized images. The key step is to design an appropriate loss function that can differentiate the influence of input data, global inputs and local inputs. With this design, our method accepts no inputs, or global inputs, or local inputs, or both global and local inputs, which is not supported in previous deep colorization methods. In addition, we propose a global color theme recommendation system to help users determine global inputs. Experimental results shows that our methods can better control the colorized images and generate state-of-art results.

Connected through hyperedge HE-1 (induced by source paper "salient object detection with lossless feature reflection and weighted structural loss"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "agile amulet real time salient object detection with contextual attention"
    - "boundary guided feature aggregation network for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"
    - "two stream flow guided convolutional attention networks for action recognition"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "coloring with words guiding image colorization through text based palette generation"):
  with members:
    - "attention is all you need"
    - "text2action generative adversarial synthesis from language to action"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "learning deep representations of fine grained visual descriptions"

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)