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: "fast zero shot image tagging"
  Abstract: The well-known word analogy experiments show that the recent word vectors capture fine-grained linguistic regularities in words by linear vector offsets, but it is unclear how well the simple vector offsets can encode visual regularities over words. We study a particular image-word relevance relation in this paper. Our results show that the word vectors of relevant tags for a given image rank ahead of the irrelevant tags, along a principal direction in the word vector space. Inspired by this observation, we propose to solve image tagging by estimating the principal direction for an image. Particularly, we exploit linear mappings and nonlinear deep neural networks to approximate the principal direction from an input image. We arrive at a quite versatile tagging model. It runs fast given a test image, in constant time w.r.t. the training set size. It not only gives superior performance for the conventional tagging task on the NUSWIDE dataset, but also outperforms competitive baselines on annotating images with previously unseen tags.

Connected through hyperedge HE-1 (induced by source paper "captioning images with novel objects via online vocabulary expansion"):
  with members:
    - "image captioning with unseen objects"
    - "large scale long tailed recognition in an open world"
    - "pointing novel objects in image captioning"
    - "neural baby talk"
    - "deep contextualized word representations"
    - "decoupled novel object captioner"
    - "exploring visual relationship for image captioning"
    - "partially supervised image captioning"

Connected through hyperedge HE-2 (induced by source paper "do cross modal systems leverage semantic relationships"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "voxceleb2 deep speaker recognition"
    - "learning cross modal deep embeddings for multi object image retrieval using text and sketch"
    - "learning two branch neural networks for image text matching tasks"
    - "dual path convolutional image text embedding with instance loss"
    - "visually aligned word embeddings for improving zero shot learning"
    - "look listen and learn"
    - "representations of language in a model of visually grounded speech signal"

Connected through hyperedge HE-3 (induced by source paper "deep residual output layers for neural language generation"):
  with members:
    - "breaking the softmax bottleneck via learnable monotonic pointwise non linearities"
    - "direct output connection for a high rank language model"
    - "beyond weight tying learning joint input output embeddings for neural machine translation"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"

Connected through hyperedge HE-4 (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"
    - "an empirical evaluation of visual question answering for novel objects"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs"):
  with members:
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "simple online and realtime tracking with a deep association metric"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "simple online and realtime tracking"
    - "person re identification by discriminative selection in video ranking"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"
    - "zero shot learning via semantic similarity embedding"

Connected through hyperedge HE-6 (induced by source paper "automatic malware description via attribute tagging and similarity embedding"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "are facial attributes adversarially robust"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity"):
  with members:
    - "stacked cross attention for image text matching"
    - "a multimodal approach to predict social media popularity"
    - "tagging like humans diverse and distinct image annotation"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "neural collaborative filtering"
    - "use of deep learning in modern recommendation system a summary of recent works"
    - "stack captioning coarse to fine learning for image captioning"

Connected through hyperedge HE-8 (induced by source paper "deep multiple instance learning for zero shot image tagging"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "deep patch learning for weakly supervised object classification and discovery"

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)