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: "knowledge embedded routing network for scene graph generation"
  Abstract: To understand a scene in depth not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since the distribution of real-world relationships is seriously unbalanced, existing methods perform quite poorly for the less frequent relationships. In this work, we find that the statistical correlations between object pairs and their relationships can effectively regularize semantic space and make prediction less ambiguous, and thus well address the unbalanced distribution issue. To achieve this, we incorporate these statistical correlations into deep neural networks to facilitate scene graph generation by developing a Knowledge-Embedded Routing Network. More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions. Extensive experiments on the large-scale Visual Genome dataset demonstrate the superiority of the proposed method over current state-of-the-art competitors.

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"
    - "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"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "bridging knowledge graphs to generate scene graphs"):
  with members:
    - "entity relation and event extraction with contextualized span representations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning by abstraction the neural state machine"
    - "an empirical study on leveraging scene graphs for visual question answering"
    - "class balanced loss based on effective number of samples"
    - "question answering as global reasoning over semantic abstractions"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "linknet relational embedding for scene graph"

Connected through hyperedge HE-3 (induced by source paper "unbiased scene graph generation via rich and fair semantic extraction"):
  with members:
    - "graph r cnn for scene graph generation"
    - "attentive relational networks for mapping images to scene graphs"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-4 (induced by source paper "learning by abstraction the neural state machine"):
  with members:
    - "measuring compositionality in representation learning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "answer them all toward universal visual question answering models"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "meta module network for compositional visual reasoning"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "learning by abstraction the neural state machine"
    - "language conditioned graph networks for relational reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"

Connected through hyperedge HE-6 (induced by source paper "knowledge graph transfer network for few shot recognition"):
  with members:
    - "variational prototyping encoder one shot learning with prototypical images"
    - "learning semantic specific graph representation for multi label image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "hybrid knowledge routed modules for large scale object detection"
    - "visual semantic navigation using scene priors"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-7 (induced by source paper "learning semantic specific graph representation for multi label image recognition"):
  with members:
    - "knowledge embedded representation learning for fine grained image recognition"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-8 (induced by source paper "leveraging auxiliary text for deep recognition of unseen visual relationships"):
  with members:
    - "large scale visual relationship understanding"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"
    - "data distillation towards omni supervised learning"
    - "scene graph generation by iterative message passing"
    - "visual translation embedding network for visual relation detection"

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)