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: "correlated and individual multi modal deep learning for rgb d object recognition"
  Abstract: In this paper, we propose a new correlated and individual multi-modal deep learning (CIMDL) method for RGB-D object recognition. Unlike most conventional RGB-D object recognition methods which extract features from the RGB and depth channels individually, our CIMDL jointly learns feature representations from raw RGB-D data with a pair of deep neural networks, so that the sharable and modal-specific information can be simultaneously exploited. Specifically, we construct a pair of deep convolutional neural networks (CNNs) for the RGB and depth data, and concatenate them at the top layer of the network with a loss function which learns a new feature space where both correlated part and the individual part of the RGB-D information are well modelled. The parameters of the whole networks are updated by using the back-propagation criterion. Experimental results on two widely used RGB-D object image benchmark datasets clearly show that our method outperforms state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "learning to see across domains and modalities"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "generalizing across domains via cross gradient training"
    - "learning from noisy web data with category level supervision"
    - "kitting in the wild through online domain adaptation"
    - "robust place categorization with deep domain generalization"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"

Connected through hyperedge HE-2 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi view intact space learning"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "learning effective rgb d representations for scene recognition"):
  with members:
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-4 (induced by source paper "recurrent convolutional fusion for rgb d object recognition"):
  with members:
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "mask r cnn"
    - "a deep representation for depth images from synthetic data"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "deep residual learning for image recognition"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-5 (induced by source paper "a recurrent multi scale approach to rbg d object recognition"):
  with members:
    - "recurrent convolutional fusion for rgb d object recognition"
    - "mask r cnn"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a deep representation for depth images from synthetic data"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-6 (induced by source paper "de 2 co deep depth colorization"):
  with members:
    - "weakly supervised dcnn for rgb d object recognition in real world applications which lack large scale annotated training data"
    - "learning representations for automatic colorization"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "a deep representation for depth images from synthetic data"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "multimodal deep learning for robust rgb d object recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "improving heterogeneous face recognition with conditional adversarial networks"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "deepid3 face recognition with very deep neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional generative adversarial nets"
    - "learning face representation from scratch"

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.CV(Computer Vision and Pattern Recognition)