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: "straight to shapes real time detection of encoded shapes"
  Abstract: Current object detection approaches predict bounding boxes, but these provide little instance-specific information beyond location, scale and aspect ratio. In this work, we propose to directly regress to objects' shapes in addition to their bounding boxes and categories. It is crucial to find an appropriate shape representation that is compact and decodable, and in which objects can be compared for higher-order concepts such as view similarity, pose variation and occlusion. To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors. This yields what to the best of our knowledge is the first real-time shape prediction network, running at ~35 FPS on a high-end desktop. With higher-order shape reasoning well-integrated into the network pipeline, the network shows the useful practical quality of generalising to unseen categories similar to the ones in the training set, something that most existing approaches fail to handle.

Connected through hyperedge HE-1 (induced by source paper "deep snake for real time instance segmentation"):
  with members:
    - "dense reppoints representing visual objects with dense point sets"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "objects as points"
    - "fast interactive object annotation with curve gcn"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "explicit shape encoding for real time instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "patchperpix for instance segmentation"):
  with members:
    - "star convex polyhedra for 3d object detection and segmentation in microscopy"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "tensormask a foundation for dense object segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "cell detection with star convex polygons"
    - "semi convolutional operators for instance segmentation"
    - "learned watershed end to end learning of seeded segmentation"
    - "instance sensitive fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "yolact better real time instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "yolov3 an incremental improvement"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"

Connected through hyperedge HE-4 (induced by source paper "yolact real time instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "icnet for real time semantic segmentation on high resolution images"

Connected through hyperedge HE-5 (induced by source paper "explicit shape encoding for real time instance segmentation"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bag of tricks for image classification with convolutional neural networks"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "yolov3 an incremental improvement"
    - "deep rnn framework for visual sequential applications"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "semi convolutional operators for instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "straight to shapes real time instance segmentation made more accurate"):
  with members:
    - "vision meets drones a challenge"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "bottom up instance segmentation using deep higher order crfs"
    - "boundary aware instance segmentation"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "zero shot object detection learning to simultaneously recognize and localize novel concepts"):
  with members:
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-8 (induced by source paper "polarity loss for zero shot object detection"):
  with members:
    - "zero shot detection"
    - "deep multiple instance learning for zero shot image tagging"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "deep feature pyramid reconfiguration for object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

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)