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: "one shot segmentation in clutter"
  Abstract: We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dataset, which we call $\textit{cluttered Omniglot}$. Using a baseline architecture combining a Siamese embedding for detection with a U-net for segmentation we show that increasing levels of clutter make the task progressively harder. Using oracle models with access to various amounts of ground-truth information, we evaluate different aspects of the problem and show that in this kind of visual search task, detection and segmentation are two intertwined problems, the solution to each of which helps solving the other. We therefore introduce $\textit{MaskNet}$, an improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects. Our findings suggest that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes.

Connected through hyperedge HE-1 (induced by source paper "few shot object detection with attention rpn and multi relation detector"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few example object detection with model communication"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-2 (induced by source paper "mechanical search multi step retrieval of a target object occluded by clutter"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "visual representations for semantic target driven navigation"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "interactive perception leveraging action in perception and perception in action"
    - "neural task programming learning to generalize across hierarchical tasks"

Connected through hyperedge HE-3 (induced by source paper "disentangling neural mechanisms for perceptual grouping"):
  with members:
    - "deep predictive coding network for object recognition"
    - "can recurrent neural networks warp time"
    - "sample efficient image segmentation through recurrence"
    - "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "dynamic routing between capsules"
    - "understanding deep learning requires rethinking generalization"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-4 (induced by source paper "fully convolutional one shot object segmentation for industrial robotics"):
  with members:
    - "fast convergence for object detection by learning how to combine error functions"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "robotic tactile perception of object properties a review"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "deep metric learning with angular loss"
    - "interactive perception leveraging action in perception and perception in action"
    - "weakly supervised semantic segmentation based on web image co segmentation"

Connected through hyperedge HE-5 (induced by source paper "one shot texture segmentation"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "prototypical networks for few shot learning"
    - "mask r cnn"
    - "few shot learning through an information retrieval lens"
    - "texture synthesis through convolutional neural networks and spectrum constraints"
    - "semantic amodal segmentation"
    - "learning to segment object candidates"
    - "u net convolutional networks for biomedical image segmentation"

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)