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: "deep triplet neural networks with cluster cca for audio visual cross modal retrieval"
  Abstract: Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been avery interesting research issue in the filed of multimedia, information retrieval, and computer vision, anddatabase. Most existing works focus on cross-modal retrieval between text-image, text-video, and lyrics-audio.little research addresses cross-modal retrieval between audio and video due to limited audio-video paireddataset and semantic information. The main challenge of audio-visual cross-modal retrieval task focuses on learning joint embeddings from a shared subspace for computing the similarity across different modalities, were generating new representations is to maximize the correlation between audio and visual modalities space. In this work, we propose a novel deep triplet neural network with cluster-based canonical correlationanalysis (TNN-C-CCA), which is an end-to-end supervised learning architecture with audio branch and videobranch. we not only consider the matching pairs in the common space but also compute the mismatching pairs when maximizing the correlation. In particular, two significant contributions are made in this work: i) abetter representation by constructing deep triplet neural network with triplet loss for optimal projections canbe generated to maximize correlation in the shared subspace. ii) positive examples and negative examplesare used in the learning stage to improve the capability of embedding learning between audio and video. Our experiment is run over 5-fold cross-validation, where average performance is applied to demonstratethe performance of audio-video cross-modal retrieval. The experimental results achieved on two different audio-visual datasets show the proposed learning architecture with two branches outperforms the state-of-art cross-modal retrieval methods.

Connected through hyperedge HE-1 (induced by source paper "learning joint embedding for cross modal retrieval"):
  with members:
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "learning from between class examples for deep sound recognition"
    - "learnable pooling with context gating for video classification"
    - "visual to sound generating natural sound for videos in the wild"

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)