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 visual knowledge memory networks for visual question answering"
  Abstract: Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or clearly answered from visual content but require reasoning from structured human knowledge with confirmation from visual content. This paper proposes visual knowledge memory network (VKMN) to address this issue, which seamlessly incorporates structured human knowledge and deep visual features into memory networks in an end-to-end learning framework. Comparing to existing methods for leveraging external knowledge for supporting VQA, this paper stresses more on two missing mechanisms. First is the mechanism for integrating visual contents with knowledge facts. VKMN handles this issue by embedding knowledge triples (subject, relation, target) and deep visual features jointly into the visual knowledge features. Second is the mechanism for handling multiple knowledge facts expanding from question and answer pairs. VKMN stores joint embedding using key-value pair structure in the memory networks so that it is easy to handle multiple facts. Experiments show that the proposed method achieves promising results on both VQA v1.0 and v2.0 benchmarks, while outperforms state-of-the-art methods on the knowledge-reasoning related questions.

Connected through hyperedge HE-1 (induced by source paper "relation aware graph attention network for visual question answering"):
  with members:
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "differential attention for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-2 (induced by source paper "relational reasoning using prior knowledge for visual captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "improving image captioning by leveraging knowledge graphs"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "object hallucination in image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-3 (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"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "visual question reasoning on general dependency tree"
    - "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-4 (induced by source paper "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"):
  with members:
    - "what object should i use task driven object detection"
    - "scene graph generation with external knowledge and image reconstruction"
    - "robocse robot common sense embedding"
    - "recent advances in imitation learning from observation"
    - "integrating knowledge and reasoning in image understanding"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

Connected through hyperedge HE-5 (induced by source paper "multi task learning of hierarchical vision language representation"):
  with members:
    - "neural baby talk"
    - "learning to count objects in natural images for visual question answering"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "visual question generation as dual task of visual question answering"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"

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)