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: "fisher vectors derived from hybrid gaussian laplacian mixture models for image annotation"
  Abstract: In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption that different distributions should be applied for different datasets, we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. An interesting property of the Expectation-Maximization algorithm for the latter is that in the maximization step, each dimension in each component is chosen to be either a Gaussian or a Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks.

Connected through hyperedge HE-1 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"

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 "ladder loss for coherent visual semantic embedding"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "stacked cross attention for image text matching"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "vse improving visual semantic embeddings with hard negatives"
    - "cross domain image retrieval with attention modeling"
    - "local similarity aware deep feature embedding"

Connected through hyperedge HE-4 (induced by source paper "using discriminative methods to learn fashion compatibility across datasets"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"
    - "fashion forward forecasting visual style in fashion"
    - "learning compatibility across categories for heterogeneous item recommendation"
    - "automatic attribute discovery with neural activations"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "semi supervised phrase localization in a bidirectional caption image retrieval framework"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "multimodal machine learning a survey and taxonomy"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "an end to end approach to natural language object retrieval via context aware deep reinforcement learning"
    - "deep metric learning via facility location"
    - "grounding of textual phrases in images by reconstruction"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "top down visual saliency guided by captions"

Connected through hyperedge HE-6 (induced by source paper "position focused attention network for image text matching"):
  with members:
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"
    - "hadamard product for low rank bilinear pooling"
    - "linking image and text with 2 way nets"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "where to look focus regions for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "unsupervised textual grounding linking words to image concepts"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "structured attention networks"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "high order attention models for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "dual attention networks for multimodal reasoning and matching"
    - "yolo9000 better faster stronger"
    - "grounding of textual phrases in images by reconstruction"

Connected through hyperedge HE-8 (induced by source paper "picture it in your mind generating high level visual representations from textual descriptions"):
  with members:
    - "wide deep learning for recommender systems"
    - "deep image retrieval learning global representations for image search"
    - "yfcc100m the new data in multimedia research"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "skip thought vectors"
    - "deep residual learning for image recognition"
    - "microsoft coco captions data collection and evaluation server"

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)