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: "egocentric meets top view"
  Abstract: Thanks to the availability and increasing popularity of Egocentric cameras such as GoPro cameras, glasses, and etc. we have been provided with a plethora of videos captured from the first person perspective. Surveillance cameras and Unmanned Aerial Vehicles(also known as drones) also offer tremendous amount of videos, mostly with top-down or oblique view-point. Egocentric vision and top-view surveillance videos have been studied extensively in the past in the computer vision community. However, the relationship between the two has yet to be explored thoroughly. In this effort, we attempt to explore this relationship by approaching two questions. First, having a set of egocentric videos and a top-view video, can we verify if the top-view video contains all, or some of the egocentric viewers present in the egocentric set? And second, can we identify the egocentric viewers in the content of the top-view video? In other words, can we find the cameramen in the surveillance videos? These problems can become more challenging when the videos are not time-synchronous. Thus we formalize the problem in a way which handles and also estimates the unknown relative time-delays between the egocentric videos and the top-view video. We formulate the problem as a spectral graph matching instance, and jointly seek the optimal assignments and relative time-delays of the videos. As a result, we spatiotemporally localize the egocentric observers in the top-view video. We model each view (egocentric or top) using a graph, and compute the assignment and time-delays in an iterative-alternative fashion.

Connected through hyperedge HE-1 (induced by source paper "multiple human association between top and horizontal views by matching subjects spatial distributions"):
  with members:
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "identifying first person camera wearers in third person videos"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "person re identification in the wild"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "ego2top matching viewers in egocentric and top view videos"
    - "learning to rank in person re identification with metric ensembles"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-2 (induced by source paper "how much do you perceive this an analysis on perceptions of geometric features personalities and emotions in virtual humans extended version"):
  with members:
    - "using big five personality model to detect cultural aspects in crowds"

Connected through hyperedge HE-3 (induced by source paper "from third person to first person dataset and baselines for synthesis and retrieval"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "cross view image synthesis using geometry guided conditional gans"
    - "identifying first person camera wearers in third person videos"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "detecting engagement in egocentric video"
    - "generative adversarial text to image synthesis"
    - "semantic image inpainting with deep generative models"

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)