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: "learning to compose dynamic tree structures for visual contexts"
  Abstract: We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual QA 2) the dynamic structure varies from image to image and task to task, allowing more content-/task-specific message passing among objects. To construct a VCTree, we design a score function that calculates the task-dependent validity between each object pair, and the tree is the binary version of the maximum spanning tree from the score matrix. Then, visual contexts are encoded by bidirectional TreeLSTM and decoded by task-specific models. We develop a hybrid learning procedure which integrates end-task supervised learning and the tree structure reinforcement learning, where the former's evaluation result serves as a self-critic for the latter's structure exploration. Experimental results on two benchmarks, which require reasoning over contexts: Visual Genome for scene graph generation and VQA2.0 for visual Q&A, show that VCTree outperforms state-of-the-art results while discovering interpretable visual context structures.

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 "counterfactual samples synthesizing for robust visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "learning by abstraction the neural state machine"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "simple but effective techniques to reduce biases"

Connected through hyperedge HE-3 (induced by source paper "learning to collocate neural modules for image captioning"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "context aware visual policy network for fine grained image captioning"
    - "context and attribute grounded dense captioning"
    - "finding syntax in human encephalography with beam search"
    - "bilinear attention networks"
    - "neural baby talk"
    - "learning to assemble neural module tree networks for visual grounding"

Connected through hyperedge HE-4 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "information maximizing visual question generation"
    - "from recognition to cognition visual commonsense reasoning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (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"
    - "on exploring undetermined relationships for visual relationship detection"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "triplet aware scene graph embeddings"):
  with members:
    - "interactive image generation using scene graphs"
    - "obj glove scene based contextual object embedding"
    - "visual relation conscious image generation from structured text"
    - "layoutvae stochastic scene layout generation from a label set"
    - "compact scene graphs for layout composition and patch retrieval"
    - "specifying object attributes and relations in interactive scene generation"
    - "linknet relational embedding for scene graph"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-7 (induced by source paper "counterfactual critic multi agent training for scene graph generation"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "graphical contrastive losses for scene graph parsing"
    - "dense relational captioning triple stream networks for relationship based captioning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "auto encoding scene graphs for image captioning"):
  with members:
    - "panoptic segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "neural baby talk"
    - "learning to count objects in natural images for visual question answering"
    - "context aware visual policy network for sequence level image captioning"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scene graph parsing as dependency parsing"

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)