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 "distance preserving grid layouts"):
  Abstract: Distance preserving visualization techniques have emerged as one of the fundamental tools for data analysis. One example are the techniques that arrange data instances into two-dimensional grids so that the pairwise distances among the instances are preserved into the produced layouts. Currently, the state-of-the-art approaches produce such grids by solving assignment problems or using permutations to optimize cost functions. Although precise, such strategies are computationally expensive, limited to small datasets or being dependent on specialized hardware to speed up the process. In this paper, we present a new technique, called Distance-preserving Grid (DGrid), that employs a binary space partitioning process in combination with multidimensional projections to create orthogonal regular grid layouts. Our results show that DGrid is as precise as the existing state-of-the-art techniques whereas requiring only a fraction of the running time and computational resources.

Includes members:
  Member "the state of the art in cartograms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "surfacing visualization mirages")
  Member "seeing behind the camera identifying the authorship of a photograph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a report on personally identifiable sensor data from smartphone devices")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)