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 "dictionary learning for adaptive gpr landmine classification"):
  Abstract: Ground-penetrating radar (GPR) target detection and classification is a challenging task. Here, we consider online dictionary learning (DL) methods to obtain sparse representations (SR) of the GPR data to enhance feature extraction for target classification via support vector machines. Online methods are preferred because traditional batch DL like K-times singular value decomposition (K-SVD) is not scalable to high-dimensional training sets and infeasible for real-time operation. We also develop Drop-Off MINi-batch Online Dictionary Learning (DOMINODL), which exploits the fact that a lot of the training data may be correlated. The DOMINODL algorithm iteratively considers elements of the training set in small batches and drops off samples which become less relevant. For the case of abandoned anti-personnel landmines classification, we compare the performance of K-SVD with three online algorithms: classical online dictionary learning (ODL), its correlation-based variant, and DOMINODL. Our experiments with real data from L-band GPR show that online DL methods reduce learning time by 36%–93% and increase mine detection by 4%–28% over K-SVD. Our DOMINODL is the fastest and retains similar classification performance as the other two online DL approaches. We use a Kolmogorov–Smirnoff test distance and the Dvoretzky–Kiefer–Wolfowitz inequality for the selection of DL input parameters leading to enhanced classification results. To further compare with the state-of-the-art classification approaches, we evaluate a convolutional neural network (CNN) classifier, which performs worse than the proposed approach. Moreover, when the acquired samples are randomly reduced by 25%, 50%, and 75%, sparse decomposition-based classification with DL remains robust while the CNN accuracy is drastically compromised.

Includes members:
  Member "sub nyquist radar principles and prototypes":
    also connected through other hyperedges:
      (none)
  Member "on learning with shift invariant structures":
    also connected through other hyperedges:
      (none)
  Member "convolutional dictionary learning a comparative review and new algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "fast low rank shared dictionary learning for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "performance of time delay estimation in a cognitive radar":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized bayesian cramer rao inequality via information geometry of relative alpha entropy")
  Member "online dictionary learning aided target recognition in cognitive gpr":
    also connected through other hyperedges:
      (none)
  Member "trainlets dictionary learning in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning")
  Member "cognitive sub nyquist hardware prototype of a collocated mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance of time delay estimation in a cognitive radar")

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)