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: "zoom net mining deep feature interactions for visual relationship recognition"
  Abstract: Recognizing visual relationships   among any pair of localized objects is pivotal for image understanding. Previous studies have shown remarkable progress in exploiting linguistic priors or external textual information to improve the performance. In this work, we investigate an orthogonal perspective based on feature interactions. We show that by encouraging deep message propagation and interactions between local object features and global predicate features, one can achieve compelling performance in recognizing complex relationships without using any linguistic priors. To this end, we present two new pooling cells to encourage feature interactions: (i) Contrastive ROI Pooling Cell, which has a unique deROI pooling that inversely pools local object features to the corresponding area of global predicate features. (ii) Pyramid ROI Pooling Cell, which broadcasts global predicate features to reinforce local object features.The two cells constitute a Spatiality-Context-Appearance Module (SCA-M), which can be further stacked consecutively to form our final Zoom-Net.We further shed light on how one could resolve ambiguous and noisy object and predicate annotations by Intra-Hierarchical trees (IH-tree). Extensive experiments conducted on Visual Genome dataset demonstrate the effectiveness of our feature-oriented approach compared to state-of-the-art methods (Acc@1 11.42% from 8.16%) that depend on explicit modeling of linguistic interactions. We further show that SCA-M can be incorporated seamlessly into existing approaches to improve the performance by a large margin. The source code will be released on this https URL

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"
    - "graphical contrastive losses for scene graph parsing"
    - "learning by abstraction the neural state machine"
    - "repair removing representation bias by dataset resampling"
    - "two causal principles for improving visual dialog"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised visual semantic parsing"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "learning human object interactions by graph parsing neural networks"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "explainable and explicit visual reasoning over scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-3 (induced by source paper "avr attention based salient visual relationship detection"):
  with members:
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "deep group shuffling random walk for person re identification"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "exploring visual relationship for image captioning"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "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"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "improving referring expression grounding with cross modal attention guided erasing"):
  with members:
    - "bilinear attention networks"
    - "dropblock a regularization method for convolutional networks"
    - "stacked cross attention for image text matching"
    - "adversarial complementary learning for weakly supervised object localization"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "mattnet modular attention network for referring expression comprehension"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual reasoning with multi hop feature modulation"

Connected through hyperedge HE-6 (induced by source paper "graphical contrastive losses for scene graph parsing"):
  with members:
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "graph r cnn for scene graph generation"
    - "mattnet modular attention network for referring expression comprehension"
    - "large scale visual relationship understanding"
    - "an interpretable model for scene graph generation"
    - "query guided regression network with context policy for phrase grounding"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-7 (induced by source paper "dense relational captioning triple stream networks for relationship based captioning"):
  with members:
    - "linknet relational embedding for scene graph"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "inferring and executing programs for visual reasoning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-8 (induced by source paper "context and attribute grounded dense captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "discriminability objective for training descriptive captions"
    - "modeling visual context is key to augmenting object detection datasets"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"

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)