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: "care about you towards large scale human centric visual relationship detection"
  Abstract: Visual relationship detection aims to capture interactions between pairs of objects in images. Relationships between objects and humans represent a particularly important subset of this problem, with implications for challenges such as understanding human behaviour, and identifying affordances, amongst others. In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotation (nearly 10K categories) than the previous released datasets. #R##N#This large label space better reflects the reality of human-object interactions, but gives rise to a long-tail distribution problem, which in turn demands a zero-shot approach to labels appearing only in the test set. This is the first time this issue has been addressed. We propose a webly-supervised approach to these problems and demonstrate that the proposed model provides a strong baseline on our HCVRD dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on class imbalance and background filtering in visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "visual relationship prediction via label clustering and incorporation of depth information"
    - "image generation from scene graphs"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "vip cnn visual phrase guided convolutional neural network"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (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-4 (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"
    - "pairwise body part attention for recognizing human object interactions"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-5 (induced by source paper "hake human activity knowledge engine"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "learning to detect human object interactions"
    - "attentional pooling for action recognition"
    - "rmpe regional multi person pose estimation"

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"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "zoom net mining deep feature interactions for visual relationship recognition"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "scene graph generation from objects phrases and region captions"
    - "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"

Connected through hyperedge HE-8 (induced by source paper "scene graph generation from objects phrases and region captions"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "towards context aware interaction recognition"
    - "person search with natural language description"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"
    - "towards diverse and natural image descriptions via a conditional gan"

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)