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: "fluid annotation a human machine collaboration interface for full image annotation"
  Abstract: We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. Fluid annotation is based on three principles:(I) Strong Machine-Learning aid. We start from the output of a strong neural network model, which the annotator can edit by correcting the labels of existing regions, adding new regions to cover missing objects, and removing incorrect regions.The edit operations are also assisted by the model.(II) Full image annotation in a single pass. As opposed to performing a series of small annotation tasks in isolation [51,68], we propose a unified interface for full image annotation in a single pass.(III) Empower the annotator.We empower the annotator to choose what to annotate and in which order. This enables concentrating on what the ma-chine does not already know, i.e. putting human effort only on the errors it made. This helps using the annotation budget effectively.   Through extensive experiments on the COCO+Stuff dataset [11,51], we demonstrate that Fluid Annotation leads to accurate an-notations very efficiently, taking 3x less annotation time than the popular LabelMe interface [70].

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (induced by source paper "invisible marker automatic annotation of segmentation masks for object manipulation"):
  with members:
    - "affordance learning for end to end visuomotor robot control"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "detection and tracking of liquids with fully convolutional networks"
    - "coco stuff thing and stuff classes in context"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "mots multi object tracking and segmentation"):
  with members:
    - "panoptic segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "how transferable are the datasets collected by active learners"
    - "practical obstacles to deploying active learning"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "3d scene graph a structure for unified semantics 3d space and camera"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "learning human object interactions by graph parsing neural networks"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "blitznet a real time deep network for scene understanding"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-5 (induced by source paper "panoptic image annotation with a collaborative assistant"):
  with members:
    - "panoptic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "guide me interacting with deep networks"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-6 (induced by source paper "simco similarity based object counting"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "class agnostic counting"
    - "in defense of the triplet loss for person re identification"
    - "how intelligent are convolutional neural networks"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"
    - "representation learning by learning to count"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "machine assisted annotation of forensic imagery"):
  with members:
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "coco stuff thing and stuff classes in context"

Connected through hyperedge HE-8 (induced by source paper "invisible marker automatic annotation for object manipulation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "virtual to real learning to control in visual semantic segmentation"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "matterport3d learning from rgb d data in indoor environments"
    - "detection and tracking of liquids with fully convolutional networks"
    - "coco stuff thing and stuff classes in context"
    - "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)