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: "graphical contrastive losses for scene graph parsing"
  Abstract: Most scene graph parsers use a two-stage pipeline to detect visual relationships: the first stage detects entities, and the second predicts the predicate for each entity pair using a softmax distribution. We find that such pipelines, trained with only a cross entropy loss over predicate classes, suffer from two common errors. The first, Entity Instance Confusion, occurs when the model confuses multiple instances of the same type of entity (e.g. multiple cups). The second, Proximal Relationship Ambiguity, arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate, and the model struggles to infer the correct subject-object pairings (e.g. mis-pairing musicians and their instruments). We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph parsing problem, collectively termed the Graphical Contrastive Losses. These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion. We further construct a relationship detector, called RelDN, using the aforementioned pipeline to demonstrate the efficacy of our proposed losses. Our model outperforms the winning method of the OpenImages Relationship Detection Challenge by 4.7\% (16.5\% relative) on the test set. We also show improved results over the best previous methods on the Visual Genome and Visual Relationship Detection datasets.

Connected through hyperedge HE-1 (induced by source paper "unbiased scene graph generation from biased training"):
  with members:
    - "visual commonsense r cnn"
    - "deconfounded image captioning a causal retrospect"
    - "knowledge embedded routing network for scene graph generation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning by abstraction the neural state machine"
    - "repair removing representation bias by dataset resampling"
    - "two causal principles for improving visual dialog"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs"):
  with members:
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "robust change captioning"
    - "describing like humans on diversity in image captioning"
    - "nocaps novel object captioning at scale"
    - "intention oriented image captions with guiding objects"
    - "neural baby talk"
    - "semstyle learning to generate stylised image captions using unaligned text"

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"
    - "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"
    - "hybrid task cascade for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"
    - "explainable video action reasoning via prior knowledge and state transitions"

Connected through hyperedge HE-5 (induced by source paper "motion aware feature for improved video anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "towards universal representation for unseen action recognition"
    - "deepvs a deep learning based video saliency prediction approach"
    - "large scale visual relationship understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-6 (induced by source paper "ppdm parallel point detection and matching for real time human object interaction detection"):
  with members:
    - "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"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "visual relationship detection with low rank non negative tensor decomposition"):
  with members:
    - "on exploring undetermined relationships for visual relationship detection"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "learning effective visual relationship detector on 1 gpu"):
  with members:
    - "deep high resolution representation learning for visual recognition"
    - "deep high resolution representation learning for human pose estimation"
    - "scene graph prediction with limited labels"
    - "graph r cnn for scene graph generation"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "neural motifs scene graph parsing with global context"

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)