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: "multi instance visual semantic embedding"
  Abstract: Visual-semantic embedding models have been recently proposed and shown to be effective for image classification and zero-shot learning, by mapping images into a continuous semantic label space. Although several approaches have been proposed for single-label embedding tasks, handling images with multiple labels (which is a more general setting) still remains an open problem, mainly due to the complex underlying corresponding relationship between image and its labels. In this work, we present Multi-Instance visual-semantic Embedding model (MIE) for embedding images associated with either single or multiple labels. Our model discovers and maps semantically-meaningful image subregions to their corresponding labels. And we demonstrate the superiority of our method over the state-of-the-art on two tasks, including multi-label image annotation and zero-shot learning.

Connected through hyperedge HE-1 (induced by source paper "polysemous visual semantic embedding for cross modal retrieval"):
  with members:
    - "on the convergence of adam and beyond"
    - "image2gif generating cinemagraphs using recurrent deep q networks"
    - "stacked cross attention for image text matching"
    - "cross modal retrieval with implicit concept association"
    - "modeling uncertainty with hedged instance embedding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "attention is all you need"
    - "learning robust visual semantic embeddings"

Connected through hyperedge HE-2 (induced by source paper "univse robust visual semantic embeddings via structured semantic representations"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "image generation from scene graphs"
    - "foil it find one mismatch between image and language caption"
    - "vse improving visual semantic embeddings with hard negatives"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (induced by source paper "zero shot learning for audio based music classification and tagging"):
  with members:
    - "multi label zero shot human action recognition via joint latent embedding"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "representation learning of music using artist labels"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "fma a dataset for music analysis"
    - "multi label zero shot learning via concept embedding"
    - "end to end localization and ranking for relative attributes"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-4 (induced by source paper "polarity loss for zero shot object detection"):
  with members:
    - "zero shot detection"
    - "deep multiple instance learning for zero shot image tagging"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "deep feature pyramid reconfiguration for object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "learning image conditioned label space for multilabel classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "fast zero shot image tagging"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "vse ens visual semantic embeddings with efficient negative sampling"):
  with members:
    - "automatic image annotation via label transfer in the semantic space"
    - "social collaborative retrieval"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "on the consistency of auc pairwise optimization"
    - "latent collaborative retrieval"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning based image captioning with embedding reward"):
  with members:
    - "image captioning with semantic attention"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "asynchronous methods for deep reinforcement learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "dense captioning with joint inference and visual context"

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)