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: "attention on attention architectures for visual question answering vqa"
  Abstract: Visual Question Answering (VQA) is an increasingly popular topic in deep learning research, requiring coordination of natural language processing and computer vision modules into a single architecture. We build upon the model which placed first in the VQA Challenge by developing thirteen new attention mechanisms and introducing a simplified classifier. We performed 300 GPU hours of extensive hyperparameter and architecture searches and were able to achieve an evaluation score of 64.78%, outperforming the existing state-of-the-art single model's validation score of 63.15%.

Connected through hyperedge HE-1 (induced by source paper "component analysis for visual question answering architectures"):
  with members:
    - "m 2 meshed memory transformer for image captioning"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "language features matter effective language representations for vision language tasks"
    - "evalai towards better evaluation systems for ai agents"
    - "murel multimodal relational reasoning for visual question answering"
    - "deep learning using rectified linear units relu"
    - "stacked cross attention for image text matching"
    - "generalized hadamard product fusion operators for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "visual entailment a novel task for fine grained image understanding"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bilinear attention networks"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "visual reasoning by progressive module networks"
    - "grounded textual entailment"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-3 (induced by source paper "holistic multi modal memory network for movie question answering"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "tvqa localized compositional video question answering"
    - "decoupled novel object captioner"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "early fusion for goal directed robotic vision"):
  with members:
    - "chalet cornell house agent learning environment"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "mapping navigation instructions to continuous control actions with position visitation prediction"
    - "a simple neural network module for relational reasoning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-5 (induced by source paper "joint image captioning and question answering"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "diverse beam search decoding diverse solutions from neural sequence models"

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)