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: "weakly supervised dcnn for rgb d object recognition in real world applications which lack large scale annotated training data"
  Abstract: This paper addresses the problem of RGBD object recognition in real-world applications, where large amounts of annotated training data are typically unavailable. To overcome this problem, we propose a novel, weakly-supervised learning architecture (DCNN-GPC) which combines parametric models (a pair of Deep Convolutional Neural Networks (DCNN) for RGB and D modalities) with non-parametric models (Gaussian Process Classification). Our system is initially trained using a small amount of labeled data, and then automatically prop- agates labels to large-scale unlabeled data. We first run 3D- based objectness detection on RGBD videos to acquire many unlabeled object proposals, and then employ DCNN-GPC to label them. As a result, our multi-modal DCNN can be trained end-to-end using only a small amount of human annotation. Finally, our 3D-based objectness detection and multi-modal DCNN are integrated into a real-time detection and recognition pipeline. In our approach, bounding-box annotations are not required and boundary-aware detection is achieved. We also propose a novel way to pretrain a DCNN for the depth modality, by training on virtual depth images projected from CAD models. We pretrain our multi-modal DCNN on public 3D datasets, achieving performance comparable to state-of-the-art methods on Washington RGBS Dataset. We then finetune the network by further training on a small amount of annotated data from our novel dataset of industrial objects (nuclear waste simulants). Our weakly supervised approach has demonstrated to be highly effective in solving a novel RGBD object recognition application which lacks of human annotations.

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 "learning monocular visual odometry with dense 3d mapping from dense 3d flow"):
  with members:
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-3 (induced by source paper "de 2 co deep depth colorization"):
  with members:
    - "learning representations for automatic colorization"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "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-4 (induced by source paper "a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition"):
  with members:
    - "geometry informed material recognition"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "material recognition from local appearance in global context"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "conditional random fields as recurrent neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "material recognition in the wild with the materials in context database"

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)