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: "one shot joint colocalization and cosegmentation"
  Abstract: This paper presents a novel framework in which image cosegmentation and colocalization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner. In contrast to multi-task learning paradigm that learns similar tasks using a shared representation, the proposed framework leverages two representations at different levels and simultaneously discriminates between foreground and background at the bounding box and superpixel level using discriminative clustering. We show empirically that constraining the two problems at different scales enables the transfer of semantic localization cues to improve cosegmentation output whereas local appearance based segmentation cues help colocalization. The unified framework outperforms strong baseline approaches, of learning the two problems separately, by a large margin on four benchmark datasets. Furthermore, it obtains competitive results compared to the state of the art for cosegmentation on two benchmark datasets and second best result for colocalization on Pascal VOC 2007.

Connected through hyperedge HE-1 (induced by source paper "one shot segmentation in clutter"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "unsupervised deep object discovery for instance recognition"
    - "deep extreme cut from extreme points to object segmentation"
    - "prototypical networks for few shot learning"
    - "webvision challenge visual learning and understanding with web data"
    - "attentive recurrent comparators"
    - "rethinking atrous convolution for semantic image segmentation"
    - "few shot learning through an information retrieval lens"

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)