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: "aligned image word representations improve inductive transfer across vision language tasks"
  Abstract: An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multi-task learning. In particular, the task of visual recognition is aligned to the task of visual question answering by forcing each to use the same word-region embeddings. We show this leads to greater inductive transfer from recognition to VQA than standard multitask learning. Visual recognition also improves, especially for categories that have relatively few recognition training labels but appear often in the VQA setting. Thus, our paper takes a small step towards creating more general vision systems by showing the benefit of interpretable, flexible, and trainable core representations.

Connected through hyperedge HE-1 (induced by source paper "vico word embeddings from visual co occurrences"):
  with members:
    - "flipdial a generative model for two way visual dialogue"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning type aware embeddings for fashion compatibility"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "event2mind commonsense inference on events intents and reactions"

Connected through hyperedge HE-2 (induced by source paper "embodied multimodal multitask learning"):
  with members:
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "compositional attention networks for machine reasoning"
    - "the sound of pixels"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "an unsupervised autoregressive model for speech representation learning"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "deep contextualized word representations"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "unsupervised textual grounding linking words to image concepts"

Connected through hyperedge HE-4 (induced by source paper "drill down interactive retrieval of complex scenes using natural language queries"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "stacked cross attention for image text matching"
    - "chat crowd a dialog based platform for visual layout composition"
    - "dialog based interactive image retrieval"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning fashion compatibility with bidirectional lstms"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-5 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "zero shot object detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "recent advances in zero shot recognition"
    - "visually aligned word embeddings for improving zero shot learning"

Connected through hyperedge HE-7 (induced by source paper "transfer learning via unsupervised task discovery for visual question answering"):
  with members:
    - "neural baby talk"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "dvqa understanding data visualizations via question answering"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "extreme clicking for efficient object annotation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-8 (induced by source paper "imagine this scripts to compositions to videos"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "learning to generate long term future via hierarchical prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "attentive semantic video generation using captions"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "photographic image synthesis with cascaded refinement networks"
    - "auto painter cartoon image generation from sketch by using conditional generative adversarial networks"

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)