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: "sequential score adaptation with extreme value theory for robust railway track inspection"
  Abstract: Periodic inspections are necessary to keep railroad tracks in state of good repair and prevent train accidents. Automatic track inspection using machine vision technology has become a very effective inspection tool. Because of its non-contact nature, this technology can be deployed on virtually any railway vehicle to continuously survey the tracks and send exception reports to track maintenance personnel. However, as appearance and imaging conditions vary, false alarm rates can dramatically change, making it difficult to select a good operating point. In this paper, we use extreme value theory (EVT) within a Bayesian framework to optimally adjust the sensitivity of anomaly detectors. We show that by approximating the lower tail of the probability density function (PDF) of the scores with an Exponential distribution (a special case of the Generalized Pareto distribution), and using the Gamma conjugate prior learned from the training data, it is possible to reduce the variability in false alarm rate and improve the overall performance. This method has shown an increase in the defect detection rate of rail fasteners in the presence of clutter (at PFA 0.1%) from 95.40% to 99.26% on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.

Connected through hyperedge HE-1 (induced by source paper "the extreme value machine"):
  with members:
    - "sparse representation based open set recognition"
    - "towards open world recognition"
    - "cloudcv large scale distributed computer vision as a cloud service"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-2 (induced by source paper "sparse representation based open set recognition"):
  with members:
    - "toward guaranteed illumination models for non convex objects"

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)