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: "differential attention for visual question answering"
  Abstract: In this paper we aim to answer questions based on images when provided with a dataset of question-answer pairs for a number of images during training. A number of methods have focused on solving this problem by using image based attention. This is done by focusing on a specific part of the image while answering the question. Humans also do so when solving this problem. However, the regions that the previous systems focus on are not correlated with the regions that humans focus on. The accuracy is limited due to this drawback. In this paper, we propose to solve this problem by using an exemplar based method. We obtain one or more supporting and opposing exemplars to obtain a differential attention region. This differential attention is closer to human attention than other image based attention methods. It also helps in obtaining improved accuracy when answering questions. The method is evaluated on challenging benchmark datasets. We perform better than other image based attention methods and are competitive with other state of the art methods that focus on both image and questions.

Connected through hyperedge HE-1 (induced by source paper "robust explanations for visual question answering"):
  with members:
    - "explanation vs attention a two player game to obtain attention for vqa"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "toward controlled generation of text"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "uncertainty based class activation maps for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"

Connected through hyperedge HE-3 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "ivqa inverse visual question answering"
    - "an analysis of visual question answering algorithms"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (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"
    - "learning visual knowledge memory networks for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-5 (induced by source paper "probabilistic framework for solving visual dialog"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "learning to count objects in natural images for visual question answering"
    - "visual coreference resolution in visual dialog using neural module networks"

Connected through hyperedge HE-6 (induced by source paper "looking back at labels a class based domain adaptation technique"):
  with members:
    - "adversarial adaptation of scene graph models for understanding civic issues"
    - "beyond sharing weights for deep domain adaptation"
    - "multimodal differential network for visual question generation"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"

Connected through hyperedge HE-7 (induced by source paper "deep exemplar networks for vqa and vqg"):
  with members:
    - "multimodal differential network for visual question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "creativity generating diverse questions using variational autoencoders"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "hadamard product for low rank bilinear pooling"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-8 (induced by source paper "attending to discriminative certainty for domain adaptation"):
  with members:
    - "adversarial adaptation of scene graph models for understanding civic issues"
    - "looking back at labels a class based domain adaptation technique"
    - "multimodal differential network for visual question generation"
    - "deep visual domain adaptation a survey"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"

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)