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: "visual question reasoning on general dependency tree"
  Abstract: The collaborative reasoning for understanding each image-question pair is very critical but under-explored for an interpretable Visual Question Answering (VQA) system. Although very recent works also tried the explicit compositional processes to assemble multiple sub-tasks embedded in the questions, their models heavily rely on the annotations or hand-crafted rules to obtain valid reasoning layout, leading to either heavy labor or poor performance on composition reasoning. In this paper, to enable global context reasoning for better aligning image and language domains in diverse and unrestricted cases, we propose a novel reasoning network called Adversarial Composition Modular Network (ACMN). This network comprises of two collaborative modules: i) an adversarial attention module to exploit the local visual evidence for each word parsed from the question; ii) a residual composition module to compose the previously mined evidence. Given a dependency parse tree for each question, the adversarial attention module progressively discovers salient regions of one word by densely combining regions of child word nodes in an adversarial manner. Then residual composition module merges the hidden representations of an arbitrary number of children through sum pooling and residual connection. Our ACMN is thus capable of building an interpretable VQA system that gradually dives the image cues following a question-driven reasoning route and makes global reasoning by incorporating the learned knowledge of all attention modules in a principled manner. Experiments on relational datasets demonstrate the superiority of our ACMN and visualization results show the explainable capability of our reasoning system.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "clevrer collision events for video representation and reasoning"):
  with members:
    - "cater a diagnostic dataset for compositional actions and temporal reasoning"
    - "monet unsupervised scene decomposition and representation"
    - "cophy counterfactual learning of physical dynamics"
    - "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"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood"):
  with members:
    - "learning to compose and reason with language tree structures for visual grounding"
    - "using syntax to ground referring expressions in natural images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "relational inductive biases deep learning and graph networks"
    - "auto encoding scene graphs for image captioning"

Connected through hyperedge HE-5 (induced by source paper "dynamic graph attention for referring expression comprehension"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "raven a dataset for relational and analogical visual reasoning"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "structured attentions for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "explainable high order visual question reasoning a new benchmark and knowledge routed network"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "learning visual knowledge memory networks for visual question answering"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-8 (induced by source paper "deep tree learning for zero shot face anti spoofing"):
  with members:
    - "generative adversarial image synthesis with decision tree latent controller"
    - "group normalization"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "tree to tree neural networks for program translation"
    - "dense face alignment"
    - "learning a deep embedding model for zero shot learning"
    - "learn convolutional neural network for face anti spoofing"
    - "zero shot learning through cross modal transfer"

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)