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: "visual semantic role labeling"
  Abstract: In this paper we introduce the problem of Visual Semantic Role Labeling: given an image we want to detect people doing actions and localize the objects of interaction. Classical approaches to action recognition either study the task of action classification at the image or video clip level or at best produce a bounding box around the person doing the action. We believe such an output is inadequate and a complete understanding can only come when we are able to associate objects in the scene to the different semantic roles of the action. To enable progress towards this goal, we annotate a dataset of 16K people instances in 10K images with actions they are doing and associate objects in the scene with different semantic roles for each action. Finally, we provide a set of baseline algorithms for this task and analyze error modes providing directions for future work.

Connected through hyperedge HE-1 (induced by source paper "visual semantic graph attention networks for human object interaction detection"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "pose aware multi level feature network for human object interaction detection"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "detecting human object interactions via functional generalization"
    - "relational action forecasting"
    - "a comprehensive survey on graph neural networks"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "ican instance centric attention network for human object interaction detection"

Connected through hyperedge HE-2 (induced by source paper "cascaded human object interaction recognition"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "reasoning visual dialogs with structural and partial observations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "graphical contrastive losses for scene graph parsing"
    - "ppdm parallel point detection and matching for real time human object interaction detection"
    - "pose aware multi level feature network for human object interaction detection"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "learning actor relation graphs for group activity recognition"

Connected through hyperedge HE-3 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions"):
  with members:
    - "learning actor relation graphs for group activity recognition"
    - "relation aware graph attention network for visual question answering"
    - "actor conditioned attention maps for video action detection"
    - "ican instance centric attention network for human object interaction detection"
    - "unified perceptual parsing for scene understanding"
    - "detecting visual relationships using box attention"
    - "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 "classifying all interacting pairs in a single shot"):
  with members:
    - "detecting human object interactions via functional generalization"
    - "detecting unseen visual relations using analogies"
    - "ican instance centric attention network for human object interaction detection"
    - "detecting visual relationships using box attention"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "object level visual reasoning in videos"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-5 (induced by source paper "detecting human object interactions via functional generalization"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "interact as you intend intention driven human object interaction detection"
    - "detecting unseen visual relations using analogies"
    - "women also snowboard overcoming bias in captioning models"
    - "ican instance centric attention network for human object interaction detection"
    - "zero shot object detection by hybrid region embedding"
    - "no frills human object interaction detection factorization layout encodings and training techniques"
    - "detecting visual relationships using box attention"

Connected through hyperedge HE-6 (induced by source paper "simitate a hybrid imitation learning benchmark"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "dart noise injection for robust imitation learning"
    - "human action localization with sparse spatial supervision"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-7 (induced by source paper "pose aware multi level feature network for human object interaction detection"):
  with members:
    - "interact as you intend intention driven human object interaction detection"
    - "ican instance centric attention network for human object interaction detection"
    - "pairwise body part attention for recognizing human object interactions"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "detecting and recognizing human object interactions"
    - "scene graph generation by iterative message passing"
    - "learning to detect human object interactions"

Connected through hyperedge HE-8 (induced by source paper "deep contextual attention for human object interaction detection"):
  with members:
    - "interact as you intend intention driven human object interaction detection"
    - "ican instance centric attention network for human object interaction detection"
    - "detecting visual relationships using box attention"
    - "learning human object interactions by graph parsing neural networks"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "generative image inpainting with contextual attention"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"

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)