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 answering with memory augmented networks"
  Abstract: In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.

Connected through hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (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"
    - "learning visual knowledge memory networks for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "learning question guided video representation for multi turn video question answering"):
  with members:
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "reactive multi stage feature fusion for multimodal dialogue modeling"
    - "audio visual scene aware dialog"
    - "motion appearance co memory networks for video question answering"
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"

Connected through hyperedge HE-4 (induced by source paper "memory attended recurrent network for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "automatic stance detection using end to end memory networks"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-5 (induced by source paper "heterogeneous memory enhanced multimodal attention model for video question answering"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "a taxonomy for neural memory networks"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "multimodal residual learning for visual qa"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "tgif a new dataset and benchmark on animated gif description"
    - "leveraging video descriptions to learn video question answering"

Connected through hyperedge HE-6 (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"
    - "learning visual knowledge memory networks for visual question answering"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "dual control memory augmented neural networks for treatment recommendations"):
  with members:
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "condensed memory networks for clinical diagnostic inferencing"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "end to end memory networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "using multi task and transfer learning to solve working memory tasks"):
  with members:
    - "attention is all you need"
    - "memory augmented neural networks with wormhole connections"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural turing machines"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

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)