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: "learning multi modal similarity"
  Abstract: In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, e.g., nearest-neighbor retrieval, classification, and recommendation. Data in such regimes typically exhibits multiple modalities, such as acoustic and visual content of video. Integrating such heterogeneous data to form a holistic similarity space is therefore a key challenge to be overcome in many real-world applications. #R##N#We present a novel multiple kernel learning technique for integrating heterogeneous data into a single, unified similarity space. Our algorithm learns an optimal ensemble of kernel transfor- mations which conform to measurements of human perceptual similarity, as expressed by relative comparisons. To cope with the ubiquitous problems of subjectivity and inconsistency in multi- media similarity, we develop graph-based techniques to filter similarity measurements, resulting in a simplified and robust training procedure.

Connected through hyperedge HE-1 (induced by source paper "a similarity measure for material appearance"):
  with members:
    - "learning icons appearance similarity"
    - "on the convergence of adam and beyond"
    - "brdf estimation of complex materials with nested learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "recognizing material properties from images"
    - "revisiting temporal modeling for video based person reid"
    - "gaussian material synthesis"
    - "a lightweight approach for on the fly reflectance estimation"

Connected through hyperedge HE-2 (induced by source paper "fast stochastic ordinal embedding with variance reduction and adaptive step size"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "learning concept embeddings with combined human machine expertise"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cost effective hits for relative similarity comparisons"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-3 (induced by source paper "perceptnet learning perceptual similarity of haptic textures in presence of unorderable triplets"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "discriminative clustering with relative constraints"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep learning for surface material classification using haptic and visual information"
    - "deep learning for tactile understanding from visual and haptic data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "robust ordinal embedding from contaminated relative comparisons"):
  with members:
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "drug similarity integration through attentive multi view graph auto encoders"):
  with members:
    - "learning the kernel matrix via predictive low rank approximations"

Connected through hyperedge HE-6 (induced by source paper "adaptive hierarchical clustering using ordinal queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "a cost function for similarity based hierarchical clustering"
    - "deterministic and probabilistic binary search in graphs"
    - "local algorithms for interactive clustering"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "noisy sorting without resampling"

Connected through hyperedge HE-7 (induced by source paper "conditional similarity networks"):
  with members:
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "learning concept embeddings with combined human machine expertise"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "disentangling nonlinear perceptual embeddings with multi query triplet networks"):
  with members:
    - "learning concept embeddings with combined human machine expertise"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "draw a recurrent neural network for image generation"
    - "very deep convolutional networks for large scale image recognition"
    - "adaptively learning the crowd kernel"

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.AI(Artificial Intelligence)