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: "detecting unseen visual relations using analogies"
  Abstract: We seek to detect visual relations in images of the form of triplets t = (subject, predicate, object), such as "person riding dog", where training examples of the individual entities are available but their combinations are unseen at training. This is an important set-up due to the combinatorial nature of visual relations : collecting sufficient training data for all possible triplets would be very hard. The contributions of this work are three-fold. First, we learn a representation of visual relations that combines (i) individual embeddings for subject, object and predicate together with (ii) a visual phrase embedding that represents the relation triplet. Second, we learn how to transfer visual phrase embeddings from existing training triplets to unseen test triplets using analogies between relations that involve similar objects. Third, we demonstrate the benefits of our approach on three challenging datasets : on HICO-DET, our model achieves significant improvement over a strong baseline over both frequent and unseen triplets, and we confirm this improvement on the retrieval of unseen triplets with out-of-vocabulary predicates on COCO-a, as well as on the challenging unusual triplets of UnRel 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"
    - "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"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "visual semantic graph attention network for human object interaction detection"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "detecting human object interactions via functional generalization"
    - "a comprehensive survey on graph neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "graph r cnn for scene graph generation"
    - "how powerful are graph neural networks"
    - "deformable convolutional 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"
    - "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"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-4 (induced by source paper "exploring the semantics for visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"
    - "detecting visual relationships with deep relational 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)