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: "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
  Abstract: Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, reasoning, and GAN-based domain adaptation techniques, we introduce a novel pipeline named SRDA to obtain large quantities of training samples with very minor effort. Our pipeline is well-suited to scenes that can be scanned, i.e. most indoor and some outdoor scenarios. To evaluate our performance, we build three representative scenes and a new dataset, with 3D models of various common objects categories and annotated real-world scene images. Extensive experiments show that our pipeline can achieve decent instance segmentation performance given very low human labor cost.

Connected through hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "graspnet a large scale clustered and densely annotated datase for object grasping"
    - "reasoning visual dialogs with structural and partial observations"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "graphonomy universal human parsing via graph transfer learning"
    - "spgnet semantic prediction guidance for scene parsing"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "rgb d individual segmentation"):
  with members:
    - "lf net learning local features from images"
    - "applying domain randomization to synthetic data for object category detection"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "semi parametric image synthesis"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "convolutional neural network architecture for geometric matching"

Connected through hyperedge HE-3 (induced by source paper "instaboost boosting instance segmentation via probability map guided copy pasting"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "explicit shape encoding for real time instance segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning segmentation masks with the independence prior"
    - "modeling visual context is key to augmenting object detection datasets"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"

Connected through hyperedge HE-4 (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"
    - "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"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "deep rnn framework for visual sequential applications"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "yolov3 an incremental improvement"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "recurrent residual module for fast inference in videos"
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"

Connected through hyperedge HE-6 (induced by source paper "transferable interactiveness knowledge for human object interaction detection"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "pose flow efficient online pose tracking"
    - "ican instance centric attention network for human object interaction detection"
    - "graph r cnn for scene graph generation"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "pairwise body part attention for recognizing human object interactions"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

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)