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: "a comprehensive survey on cross modal retrieval"
  Abstract: In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. Various methods have been proposed to deal with such a problem. In this paper, we first review a number of representative methods for cross-modal retrieval and classify them into two main groups: 1) real-valued representation learning, and 2) binary representation learning. Real-valued representation learning methods aim to learn real-valued common representations for different modalities of data. To speed up the cross-modal retrieval, a number of binary representation learning methods are proposed to map different modalities of data into a common Hamming space. Then, we introduce several multimodal datasets in the community, and show the experimental results on two commonly used multimodal datasets. The comparison reveals the characteristic of different kinds of cross-modal retrieval methods, which is expected to benefit both practical applications and future research. Finally, we discuss open problems and future research directions.

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 "beyond intra modality a survey of heterogeneous person re identification"):
  with members:
    - "illumination adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "densely semantically aligned person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-3 (induced by source paper "hal improved text image matching by mitigating visual semantic hubs"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "a strong and robust baseline for text image matching"
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "improving generalization via scalable neighborhood component analysis"
    - "engaging image captioning via personality"
    - "sampling matters in deep embedding learning"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-4 (induced by source paper "modal aware features for multimodal hashing"):
  with members:
    - "deep semantic multimodal hashing network for scalable multimedia retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "robust deep multi modal learning based on gated information fusion network"
    - "cbam convolutional block attention module"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "multimodal machine learning a survey and taxonomy"
    - "non local neural networks"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-5 (induced by source paper "self supervised visual representations for cross modal retrieval"):
  with members:
    - "texttopicnet self supervised learning of visual features through embedding images on semantic text spaces"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "ambient sound provides supervision for visual learning"
    - "context encoders feature learning by inpainting"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "data dependent initializations of convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "cross modal variational auto encoder with distributed latent spaces and associators"):
  with members:
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "learning to localize sound source in visual scenes"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "cross modal deep variational hand pose estimation"
    - "multimodal generative models for scalable weakly supervised learning"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "conditional generation of multi modal data using constrained embedding space mapping"

Connected through hyperedge HE-7 (induced by source paper "diachronic cross modal embeddings"):
  with members:
    - "temporal cross media retrieval with soft smoothing"
    - "dynamic word embeddings for evolving semantic discovery"
    - "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "learning deep structure preserving image text embeddings"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-8 (induced by source paper "interactive video retrieval with dialog"):
  with members:
    - "audio visual scene aware dialog"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "the kinetics human action video dataset"
    - "word2visualvec image and video to sentence matching by visual feature prediction"

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.MM(Multimedia)