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: "on exploring undetermined relationships for visual relationship detection"
  Abstract: In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists, few methods exploit these undetermined relationships for visual relationship detection. #R##N#In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: visual, spatial, and linguistic modals. Further, the MF-URLN proposes two correlated subnetworks: one subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: the Visual Relationship Detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset.

Connected through hyperedge HE-1 (induced by source paper "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "pointing novel objects in image captioning"
    - "improving grounded natural language understanding through human robot dialog"
    - "context and attribute grounded dense captioning"
    - "connecting touch and vision via cross modal prediction"
    - "interactive visual grounding of referring expressions for human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "visual relationship detection with low rank non negative tensor decomposition"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "neural motifs scene graph parsing with global context"
    - "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"

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)