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: "cross modal similarity learning a low rank bilinear formulation"
  Abstract: The cross-media retrieval problem has received much attention in recent years due to the rapid increasing of multimedia data on the Internet. A new approach to the problem has been raised which intends to match features of different modalities directly. In this research, there are two critical issues: how to get rid of the heterogeneity between different modalities and how to match the cross-modal features of different dimensions. Recently metric learning methods show a good capability in learning a distance metric to explore the relationship between data points. However, the traditional metric learning algorithms only focus on single-modal features, which suffer difficulties in addressing the cross-modal features of different dimensions. In this paper, we propose a cross-modal similarity learning algorithm for the cross-modal feature matching. The proposed method takes a bilinear formulation, and with the nuclear-norm penalization, it achieves low-rank representation. Accordingly, the accelerated proximal gradient algorithm is successfully imported to find the optimal solution with a fast convergence rate O(1/t^2). Experiments on three well known image-text cross-media retrieval databases show that the proposed method achieves the best performance compared to the state-of-the-art algorithms.

Connected through hyperedge HE-1 (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"
    - "a comprehensive survey on cross modal retrieval"
    - "context encoders feature learning by inpainting"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"

Connected through hyperedge HE-2 (induced by source paper "is this an example image predicting the relative abstractness level of image and text"):
  with members:
    - "deep audio visual speech recognition"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "multimodal machine learning a survey and taxonomy"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "enriching word vectors with subword information"
    - "going deeper with convolutions"
    - "moddrop adaptive multi modal gesture recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning of visual features through embedding images into text topic spaces"):
  with members:
    - "ambient sound provides supervision for visual learning"
    - "a comprehensive survey on cross modal retrieval"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "data dependent initializations of convolutional neural networks"
    - "fast r cnn"
    - "convolutional clustering for unsupervised learning"
    - "learning to see by moving"

Connected through hyperedge HE-4 (induced by source paper "simple to complex cross modal learning to rank"):
  with members:
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "weakly supervised object localization using size estimates"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "deep convolutional ranking for multilabel image annotation"

Connected through hyperedge HE-5 (induced by source paper "cross domain visual matching via generalized similarity measure and feature learning"):
  with members:
    - "dari distance metric and representation integration for person verification"
    - "deep ranking for person re identification via joint representation learning"
    - "deep feature learning with relative distance comparison for person re identification"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "deep metric learning for practical person re identification"
    - "learning fine grained image similarity with deep ranking"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "representing data by sparse combination of contextual data points for classification"
    - "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect"
    - "evaluation of pose tracking accuracy in the first and second generations of microsoft kinect"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"

Connected through hyperedge HE-7 (induced by source paper "modality dependent cross media retrieval"):
  with members:
    - "a multi view embedding space for modeling internet images tags and their semantics"

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)