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: "asymmetrically weighted cca and hierarchical kernel sentence embedding for multimodal retrieval"
  Abstract: Joint modeling of language and vision has been drawing increasing interest. A multimodal data representation allowing for bidirectional retrieval of images by sentences and vice versa is a key aspect. In this paper we present three contributions in canonical correlation analysis (CCA) based multimodal retrieval. Firstly, we show that an asymmetric weighting of the canonical weights, while achieving a cross-view mapping from the search to the query space, it improves the retrieval performance. Secondly, we devise a computationally efficient model selection - crucial to generalization and stability - in the framework of the Bjork Golub algorithm for regularized CCA via spectral filtering. Finally, we introduce a Hierarchical Kernel Sentence Embedding (HKSE) that approximates Kernel CCA for a special similarity kernel between words distributions. State of the art results are obtained on MSCOCO and Flickr benchmarks when these three techniques are used in conjunction.

Connected through hyperedge HE-1 (induced by source paper "similarity of neural network representations revisited"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "skip connections eliminate singularities"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "perceptual losses for real time style transfer and super resolution"
    - "density estimation using real nvp"

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.LG(Machine Learning)