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: "ican instance centric attention network for human object interaction detection"
  Abstract: Recent years have witnessed rapid progress in detecting and recognizing individual object instances. To understand the situation in a scene, however, computers need to recognize how humans interact with surrounding objects. In this paper, we tackle the challenging task of detecting human-object interactions (HOI). Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction. To exploit these cues, we propose an instance-centric attention module that learns to dynamically highlight regions in an image conditioned on the appearance of each instance. Such an attention-based network allows us to selectively aggregate features relevant for recognizing HOIs. We validate the efficacy of the proposed network on the Verb in COCO and HICO-DET datasets and show that our approach compares favorably with the state-of-the-arts.

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"
    - "no frills human object interaction detection factorization layout encodings and training techniques"

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"
    - "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"
    - "detecting and recognizing human object interactions"

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"
    - "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"
    - "focal loss for dense object detection"

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"
    - "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"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-6 (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"
    - "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"
    - "cascaded pyramid network for multi person pose estimation"

Connected through hyperedge HE-7 (induced by source paper "deep contextual attention for human object interaction detection"):
  with members:
    - "interact as you intend intention driven 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"
    - "learning to detect human object interactions"

Connected through hyperedge HE-8 (induced by source paper "ppdm parallel point detection and matching for real time human object interaction detection"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "pose aware multi level feature network for human object interaction detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "pairwise body part attention for recognizing human object interactions"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "focal loss for dense object detection"

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)