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: "simultaneous detection and segmentation"
  Abstract: We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, top- down figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.

Connected through hyperedge HE-1 (induced by source paper "deep affinity net instance segmentation via affinity"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "instance segmentation by deep coloring"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"

Connected through hyperedge HE-2 (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-3 (induced by source paper "deeperlab single shot image parser"):
  with members:
    - "panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "affinity derivation and graph merge for instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "episodic training for domain generalization"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "self supervised generalisation with meta auxiliary learning"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-6 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-7 (induced by source paper "an analysis of pre training on object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "cornernet detecting objects as paired keypoints"
    - "do better imagenet models transfer better"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-8 (induced by source paper "a survey on deep learning based architectures for semantic segmentation on 2d images"):
  with members:
    - "panoptic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "yolact real time instance segmentation"
    - "convolutional crfs for semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic video segmentation a review on recent approaches"
    - "attention u net learning where to look for the pancreas"

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)