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: "end to end cross modality retrieval with cca projections and pairwise ranking loss"
  Abstract: Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query. The state-of-the-art approach to cross-modality retrieval relies on learning a joint embedding space of the two modalities, where items from either modality are retrieved using nearest-neighbor search. In this work, we introduce a neural network layer based on canonical correlation analysis (CCA) that learns better embedding spaces by analytically computing projections that maximize correlation. In contrast to previous approaches, the CCA layer allows us to combine existing objectives for embedding space learning, such as pairwise ranking losses, with the optimal projections of CCA. We show the effectiveness of our approach for cross-modality retrieval on three different scenarios (text-to-image, audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a multi-view network using freely learned projections optimized by a pairwise ranking loss, especially when little training data is available (the code for all three methods is released at: #N#                  https://github.com/CPJKU/cca_layer#N#                  #N#                ).

Connected through hyperedge HE-1 (induced by source paper "object detection on single monocular images through canonical correlation analysis"):
  with members:
    - "a survey of deep learning based object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "feature selective anchor free module for single shot object detection"
    - "deep rgb d canonical correlation analysis for sparse depth completion"
    - "high quality monocular depth estimation via transfer learning"
    - "focal loss for dense object detection"
    - "relation networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "multidec multi modal clustering of image caption pairs"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep clustering for unsupervised learning of visual features"
    - "improving image clustering with multiple pretrained cnn feature extractors"
    - "learning robust visual semantic embeddings"
    - "cnn features are also great at unsupervised classification"
    - "ezlearn exploiting organic supervision in large scale data annotation"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "deep multi view learning via task optimal cca"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "decorrelated batch normalization"
    - "scalable and effective deep cca via soft decorrelation"
    - "deep linear discriminant analysis"
    - "multimodal sparse representation learning and applications"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "correlational neural networks"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"

Connected through hyperedge HE-4 (induced by source paper "learning soft attention models for tempo invariant audio sheet music retrieval"):
  with members:
    - "deep polyphonic adsr piano note transcription"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "attention as a perspective for learning tempo invariant audio queries"
    - "look listen and learn"
    - "on the potential of simple framewise approaches to piano transcription"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "cross modal music retrieval and applications an overview of key methodologies"):
  with members:
    - "learning audio sheet music correspondences for score identification and offline alignment"
    - "deep learning in neural networks"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-6 (induced by source paper "learning audio sheet music correspondences for score identification and offline alignment"):
  with members:
    - "on the potential of simple framewise approaches to piano transcription"
    - "towards score following in sheet music images"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

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.IR(Information Retrieval)