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: "pairwise body part attention for recognizing human object interactions"
  Abstract: In human-object interactions (HOI) recognition, conventional methods consider the human body as a whole and pay a uniform attention to the entire body region. They ignore the fact that normally, human interacts with an object by using some parts of the body. In this paper, we argue that different body parts should be paid with different attention in HOI recognition, and the correlations between different body parts should be further considered. This is because our body parts always work collaboratively. We propose a new pairwise body-part attention model which can learn to focus on crucial parts, and their correlations for HOI recognition. A novel attention based feature selection method and a feature representation scheme that can capture pairwise correlations between body parts are introduced in the model. Our proposed approach achieved 4% improvement over the state-of-the-art results in HOI recognition on the HICO dataset. We will make our model and source codes publicly available.

Connected through hyperedge HE-1 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "an evaluation of action recognition models on epic kitchens"
    - "large scale weakly supervised pre training for video action recognition"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-3 (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-4 (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-5 (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"
    - "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-6 (induced by source paper "understanding human gaze communication by spatio temporal graph reasoning"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "deep visual attention prediction"
    - "iterative visual reasoning beyond convolutions"
    - "learning human object interactions by graph parsing neural networks"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "situation recognition with graph neural networks"
    - "a simple neural network module for relational reasoning"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-7 (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"
    - "ican instance centric attention network for human object interaction detection"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "hake human activity knowledge engine"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "learning to detect human object interactions"
    - "care about you towards large scale human centric visual relationship detection"
    - "attentional pooling for action recognition"
    - "rmpe regional multi person pose estimation"

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)