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 "a tolerant edit distance for evaluation and training of electron microscopy reconstruction algorithms"):
  Abstract: We present a measure to compare the labeling of automatic neuron reconstruction methods against ground truth. This measure, which we call tolerant edit distance (TED), is motivated by two observations: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be a parameter of the measure. (2) Non-tolerable errors have to be corrected manually. The time needed to do so should be reflected by the error measure and minimized during training. The TED finds the minimal weighted sum of split and merge errors exceeding a given tolerance criterion, and thus provides a time-to-fix estimate. Our measure works on both isotropic and anisotropic EM datasets, the results are intuitive, and errors can be localized in the volume. We also present a structured learning framework for assignment models for anisotropic neuron reconstruction and show how this framework can be used to minimize the TED on annotated training samples. Evaluated on two publicly available EM-datasets, our method shows consistently higher reconstruction accuracy, even on pre-existing measures, than other current learning methods. Furthermore, we show how an appropriately defined tolerance criterion allows us to train on skeleton (i.e., non-volumetric) annotations, which are much faster to obtain in practice.

Includes members:
  Member "small sample learning of superpixel classifiers for em segmentation extended version":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ted a tolerant edit distance for segmentation evaluation")
  Member "machine learning of hierarchical clustering to segment 2d and 3d images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient 2d neuron boundary segmentation with local topological constraints")
  Member "deep and wide multiscale recursive networks for robust image labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling distributed training of flood filling networks on hpc infrastructure for brain mapping")
  Member "objective criteria for the evaluation of clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")

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)