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: "fast mitochondria segmentation for connectomics"
  Abstract: In connectomics, scientists create the wiring diagram of a mammalian brain by identifying synaptic connections between neurons in nano-scale electron microscopy images. This allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar. However, manual analysis is not feasible since connectomics datasets can be petabytes in size. To process such large data, we present a fully automatic mitochondria detector based on a modified U-Net architecture that yields high accuracy and fast processing times. We evaluate our method on multiple real-world connectomics datasets, including an improved version of the EPFL Hippocampus mitochondria detection benchmark. Our results show a Jaccard index of up to 0.90 with inference speeds lower than 16ms for a 512x512 image tile. This speed is faster than the acquisition time of modern electron microscopes, allowing mitochondria detection in real-time. Compared to previous work, our detector ranks first among real-time methods and third overall. Our data, results, and code are freely available.

Connected through hyperedge HE-1 (induced by source paper "em net centerline aware mitochondria segmentation in em images via hierarchical view ensemble convolutional network"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "discriminative training of conditional random fields with probably submodular constraints"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "learning deep structured models"
    - "on the expected complexity of random convex hulls"

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)