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: "annotation free and one shot learning for instance segmentation of homogeneous object clusters"
  Abstract: We propose a novel approach for instance segmen- tation given an image of homogeneous object clus- ter (HOC). Our learning approach is one-shot be- cause a single video of an object instance is cap- tured and it requires no human annotation. Our in- tuition is that images of homogeneous objects can be effectively synthesized based on structure and illumination priors derived from real images. A novel solver is proposed that iteratively maximizes our structured likelihood to generate realistic im- ages of HOC. Illumination transformation scheme is applied to make the real and synthetic images share the same illumination condition. Extensive experiments and comparisons are performed to ver- ify our method. We build a dataset consisting of pixel-level annotated images of HOC. The dataset and code will be published with the paper.

Connected through hyperedge HE-1 (induced by source paper "one shot texture retrieval with global context metric"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "meta learning for semi supervised few shot classification"
    - "deep texture manifold for ground terrain recognition"
    - "one shot texture segmentation"
    - "the 2018 davis challenge on video object segmentation"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning segmentation masks with the independence prior"):
  with members:
    - "weakly supervised instance segmentation using class peak response"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "spectral normalization for generative adversarial networks"
    - "mine mutual information neural estimation"
    - "image generation from scene graphs"
    - "relation networks for object detection"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"

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)