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: "an empirical evaluation of visual question answering for novel objects"
  Abstract: We study the problem of answering questions about images in the harder setting, where the test questions and corresponding images contain novel objects, which were not queried about in the training data. Such setting is inevitable in real world-owing to the heavy tailed distribution of the visual categories, there would be some objects which would not be annotated in the train set. We show that the performance of two popular existing methods drop significantly (up to 28%) when evaluated on novel objects cf. known objects. We propose methods which use large existing external corpora of (i) unlabeled text, i.e. books, and (ii) images tagged with classes, to achieve novel object based visual question answering. We do systematic empirical studies, for both an oracle case where the novel objects are known textually, as well as a fully automatic case without any explicit knowledge of the novel objects, but with the minimal assumption that the novel objects are semantically related to the existing objects in training. The proposed methods for novel object based visual question answering are modular and can potentially be used with many visual question answering architectures. We show consistent improvements with the two popular architectures and give qualitative analysis of the cases where the model does well and of those where it fails to bring improvements.

Connected through hyperedge HE-1 (induced by source paper "unshuffling data for improved generalization"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "quantifying and alleviating the language prior problem in visual question answering"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "simple but effective techniques to reduce biases"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "actively seeking and learning from live data"):
  with members:
    - "on first order meta learning algorithms"
    - "deep nets what have they ever done for vision"
    - "natural language to structured query generation via meta learning"
    - "deep learning a critical appraisal"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-3 (induced by source paper "generative model for zero shot sketch based image retrieval"):
  with members:
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "toward controlled generation of text"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "from known to the unknown transferring knowledge to answer questions about novel visual and semantic concepts"):
  with members:
    - "reciprocal attention fusion for visual question answering"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual 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"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "hadamard product for low rank bilinear pooling"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-5 (induced by source paper "zero shot transfer vqa dataset"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "generalized zero and few shot learning via aligned variational autoencoders"):
  with members:
    - "low shot learning from imaginary data"
    - "style transfer from non parallel text by cross alignment"
    - "learning robust visual semantic embeddings"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "prototypical networks for few shot learning"
    - "improving one shot learning through fusing side information"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a zero shot framework for sketch based image retrieval"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "zero shot learning the good the bad and the ugly"
    - "fast zero shot image tagging"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-8 (induced by source paper "measuring abstract reasoning in neural networks"):
  with members:
    - "deep learning a critical appraisal"
    - "building machines that learn and think for themselves commentary on lake et al behavioral and brain sciences 2017"
    - "iq of neural networks"
    - "discovering objects and their relations from entangled scene representations"
    - "measuring the tendency of cnns to learn surface statistical regularities"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "understanding deep learning requires rethinking generalization"

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)