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: "learning dense convolutional embeddings for semantic segmentation"
  Abstract: This paper proposes a new deep convolutional neural network (DCNN) architecture#R##N#that learns pixel embeddings, such that pairwise distances between the embeddings#R##N#can be used to infer whether or not the pixels lie on the same region.#R##N#That is, for any two pixels on the same object, the embeddings are trained to be#R##N#similar; for any pair that straddles an object boundary, the embeddings are trained#R##N#to be dissimilar. Experimental results show that when this embedding network#R##N#is used in conjunction with a DCNN trained on semantic segmentation, there is a#R##N#systematic improvement in per-pixel classification accuracy. Our contributions are#R##N#integrated in the popular Caffe deep learning framework, and consist in straightforward#R##N#modifications to convolution routines. As such, they can be exploited for#R##N#any task involving convolution layers.

Connected through hyperedge HE-1 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-2 (induced by source paper "consensus feature network for scene parsing"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction"):
  with members:
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "instance segmentation of fibers from low resolution ct scans via 3d deep embedding learning"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "on the convergence of adam and beyond"
    - "learning metric graphs for neuron segmentation in electron microscopy images"
    - "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"

Connected through hyperedge HE-4 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "interpretable structure evolving lstm"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "geometry aware recurrent neural networks for active visual recognition"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning a multi view stereo machine"
    - "a dataset for developing and benchmarking active vision"
    - "unsupervised learning of depth and ego motion from video"
    - "neural map structured memory for deep reinforcement learning"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-7 (induced by source paper "learning to measure change fully convolutional siamese metric networks for scene change detection"):
  with members:
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "depth adaptive deep neural network for semantic segmentation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation aware convolutional networks using local attention masks"
    - "how far can you get by combining change detection algorithms"
    - "deformable convolutional networks"
    - "semantic instance segmentation via deep metric learning"
    - "learning deep structured network for weakly supervised change detection"

Connected through hyperedge HE-8 (induced by source paper "segmentation aware convolutional networks using local attention masks"):
  with members:
    - "semantic instance segmentation via deep metric learning"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "optical flow estimation using a spatial pyramid network"
    - "conditional image generation with pixelcnn decoders"
    - "language modeling with gated convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "hierarchical question image co attention for visual question answering"

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)