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: "searching for representative modes on hypergraphs for robust geometric model fitting"
  Abstract: In this paper, we propose a simple and effective geometric model fitting method to fit and segment multi-structure data even in the presence of severe outliers. We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs. Specifically, a hypergraph is first constructed, where the vertices represent model hypotheses and the hyperedges denote data points. The hypergraph involves higher-order similarities (instead of pairwise similarities used on a simple graph), and it can characterize complex relationships between model hypotheses and data points. In addition, we develop a hypergraph reduction technique to remove “insignificant” vertices while retaining as many “significant” vertices as possible in the hypergraph. Based on the simplified hypergraph, we then propose a novel mode-seeking algorithm to search for representative modes within reasonable time. Finally, the proposed mode-seeking algorithm detects modes according to two key elements, i.e., the weighting scores of vertices and the similarity analysis between vertices. Overall, the proposed fitting method is able to efficiently and effectively estimate the number and the parameters of model instances in the data simultaneously. Experimental results demonstrate that the proposed method achieves significant superiority over several state-of-the-art model fitting methods on both synthetic data and real images.

Connected through hyperedge HE-1 (induced by source paper "tensor entropy for uniform hypergraphs"):
  with members:
    - "principal component analysis with tensor train subspace"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "the resistance perturbation distance a metric for the analysis of dynamic networks"
    - "graph based anomaly detection and description a survey"
    - "most tensor problems are np hard"

Connected through hyperedge HE-2 (induced by source paper "progressive x efficient anytime multi model fitting algorithm"):
  with members:
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "multi class model fitting by energy minimization and mode seeking"
    - "graph cut ransac"
    - "mode seeking on hypergraphs for robust geometric model fitting"

Connected through hyperedge HE-3 (induced by source paper "fast regularity constrained plane reconstruction"):
  with members:
    - "latent ransac"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "robust and globally optimal manhattan frame estimation in near real time"

Connected through hyperedge HE-4 (induced by source paper "hypergraph optimization for multi structural geometric model fitting"):
  with members:
    - "effective sampling fast segmentation using robust geometric model fitting"
    - "regression based hypergraph learning for image clustering and classification"

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)