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: "adaptis adaptive instance selection network"
  Abstract: We present Adaptive Instance Selection network architecture for class-agnostic instance segmentation. Given an input image and a point $(x, y)$, it generates a mask for the object located at $(x, y)$. The network adapts to the input point with a help of AdaIN layers, thus producing different masks for different objects on the same image. AdaptIS generates pixel-accurate object masks, therefore it accurately segments objects of complex shape or severely occluded ones. AdaptIS can be easily combined with standard semantic segmentation pipeline to perform panoptic segmentation. To illustrate the idea, we perform experiments on a challenging toy problem with difficult occlusions. Then we extensively evaluate the method on panoptic segmentation benchmarks. We obtain state-of-the-art results on Cityscapes and Mapillary even without pretraining on COCO, and show competitive results on a challenging COCO dataset. The source code of the method and the trained models are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "knowledge distillation via adaptive instance normalization"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "few shot adversarial learning of realistic neural talking head models"
    - "xnor net improved binary neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "towards bounding box free panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "panoptic edge detection"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"

Connected through hyperedge HE-3 (induced by source paper "polytransform deep polygon transformer for instance segmentation"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "panoptic segmentation"
    - "video instance segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "hybrid task cascade for instance segmentation"
    - "deep rigid instance scene flow"

Connected through hyperedge HE-4 (induced by source paper "learning instance occlusion for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "mask scoring r cnn"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"
    - "panoptic segmentation with a joint semantic and instance segmentation network"

Connected through hyperedge HE-5 (induced by source paper "single shot panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "learning instance occlusion for panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"

Connected through hyperedge HE-6 (induced by source paper "real time panoptic segmentation from dense detections"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "seamless scene segmentation"
    - "polarmask single shot instance segmentation with polar representation"
    - "deeperlab single shot image parser"
    - "mask scoring r cnn"

Connected through hyperedge HE-7 (induced by source paper "solo segmenting objects by locations"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

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)