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: "scene graph parsing as dependency parsing"
  Abstract: In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%. We further demonstrate the effectiveness of our learned parser on image retrieval applications.

Connected through hyperedge HE-1 (induced by source paper "an empirical study on leveraging scene graphs for visual question answering"):
  with members:
    - "rethinking visual relationships for high level image understanding"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "vrr vg refocusing visually relevant relationships"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "learning cross modal context graph for visual grounding"):
  with members:
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "neural sequential phrase grounding seqground"
    - "language conditioned graph networks for relational reasoning"
    - "murel multimodal relational reasoning for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "unsupervised image captioning"

Connected through hyperedge HE-3 (induced by source paper "scene graph generation with external knowledge and image reconstruction"):
  with members:
    - "semantic correlation promoted shape variant context for segmentation"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "graph r cnn for scene graph generation"
    - "auto encoding scene graphs for image captioning"
    - "unpaired image captioning by language pivoting"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "cross modal scene graph matching for relationship aware image text retrieval"):
  with members:
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "a comprehensive survey on graph neural networks"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "stacked cross attention for image text matching"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

Connected through hyperedge HE-5 (induced by source paper "scene graph parsing by attention graph"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "arc swift a novel transition system for dependency parsing"
    - "recurrent multimodal interaction for referring image segmentation"
    - "attention correctness in neural image captioning"
    - "spice semantic propositional image caption evaluation"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-6 (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"
    - "learning to compose dynamic tree structures for visual contexts"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

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.CL(Computation and Language)