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: "parsing images of overlapping organisms with deep singling out networks"
  Abstract: This work is motivated by the mostly unsolved task of parsing biological images with multiple overlapping articulated model organisms (such as worms or larvae). We present a general approach that separates the two main challenges associated with such data, individual object shape estimation and object groups disentangling. At the core of the approach is a deep feed-forward singling-out network (SON) that is trained to map each local patch to a vectorial descriptor that is sensitive to the characteristics (e.g. shape) of a central object, while being invariant to the variability of all other surrounding elements. Given a SON, a local image patch can be matched to a gallery of isolated elements using their SON-descriptors, thus producing a hypothesis about the shape of the central element in that patch. The image-level optimization based on integer programming can then pick a subset of the hypotheses to explain (parse) the whole image and disentangle groups of organisms. #R##N#While sharing many similarities with existing "analysis-by-synthesis" approaches, our method avoids the need for stochastic search in the high-dimensional configuration space and numerous rendering operations at test-time. We show that our approach can parse microscopy images of three popular model organisms (the C.Elegans roundworms, the Drosophila larvae, and the E.Coli bacteria) even under significant crowding and overlaps between organisms. We speculate that the overall approach is applicable to a wider class of image parsing problems concerned with crowded articulated objects, for which rendering training images is possible.

Connected through hyperedge HE-1 (induced by source paper "instance segmentation of biological images using harmonic embeddings"):
  with members:
    - "instance segmentation by deep coloring"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "recurrent pixel embedding for instance grouping"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "zhores petaflops supercomputer for data driven modeling machine learning and artificial intelligence installed in skolkovo institute of science and technology"):
  with members:
    - "data driven model for the identification of the rock type at a drilling bit"
    - "deep image prior"
    - "neural codes for image retrieval"

Connected through hyperedge HE-3 (induced by source paper "semi convolutional operators for instance segmentation"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "segmentation aware convolutional networks using local attention masks"
    - "recurrent pixel embedding for instance grouping"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic instance segmentation via deep metric learning"
    - "mask r cnn"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-4 (induced by source paper "cell detection with star convex polygons"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "moral lineage tracing"
    - "straight to shapes real time detection of encoded shapes"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "cell tracking via proposal generation and selection"):
  with members:
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"
    - "what makes for effective detection proposals"
    - "feature pyramid networks for object detection"
    - "instance aware semantic segmentation via multi task network cascades"
    - "u net convolutional networks for biomedical image segmentation"
    - "detecting and tracking cells using network flow programming"
    - "faster r cnn towards real time object detection with region proposal networks"

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)