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: "stability analysis of kinetic orientation based shape descriptors"
  Abstract: We study three orientation-based shape descriptors on a set of continuously moving points P: the first principal component, the smallest oriented bounding box and the thinnest strip. Each of these shape descriptors essentially defines a cost capturing the quality of the descriptor (sum of squared distances for principal component, area for oriented bounding box, and width for strip), and uses the orientation that minimizes the cost. This optimal orientation may be very unstable as the points are moving, which is undesirable in many practical scenarios. Alternatively, we can bound the speed with which the orientation of the descriptor may change. However, this can increase the cost (and hence lower the quality) of the resulting shape descriptor. In this paper we study the trade-off between stability and quality of these shape descriptors. We first show that there is no stateless algorithm, which depends only on the input points in one time step and not on previous states, that both approximates the minimum cost of a shape descriptor and achieves bounded speed. On the other hand, if we can use the previous state of the shape descriptor to compute the new state, then we can define "chasing" algorithms that attempt to follow the optimal orientation with bounded speed. Under mild conditions, we show that chasing algorithms with sufficient bounded speed approximate the optimal cost at every time step for oriented bounding boxes and strips, but not for principal components. The analysis of such chasing algorithms is challenging and has received little attention in literature, hence we believe that our methods used to perform this analysis are of independent interest.

Connected through hyperedge HE-1 (induced by source paper "spatially and temporally coherent visual summaries"):
  with members:
    - "block crossings in storyline visualizations"

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.CG(Computational Geometry)