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: "robust deep multi modal learning based on gated information fusion network"
  Abstract: The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature levels. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of imperfect modalities. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature map by combining the intermediate features from the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The weights are determined through the convolutional layers followed by a sigmoid function and trained along with the information fusion network in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve robust performance in handling some degraded modalities, and show a significant performance improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes.

Connected through hyperedge HE-1 (induced by source paper "trained model fusion for object detection using gating network"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "yolov3 an incremental improvement"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "partial adversarial domain adaptation"
    - "edf ensemble distill and fuse for easy video labeling"
    - "rank of experts detection network ensemble"
    - "focal loss for dense object detection"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "on single source robustness in deep fusion models"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "state of the art speech recognition with sequence to sequence models"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mask r cnn"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "ssd single shot multibox detector"
    - "a deep metric for multimodal registration"

Connected through hyperedge HE-3 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "on single source robustness in deep fusion models"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "modal aware features for multimodal hashing"):
  with members:
    - "deep semantic multimodal hashing network for scalable multimedia retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cbam convolutional block attention module"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "multimodal machine learning a survey and taxonomy"
    - "non local neural networks"
    - "hashnet deep learning to hash by continuation"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-5 (induced by source paper "deep gated multi modal learning in hand object pose changes estimation using tactile and image data"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "bop benchmark for 6d object pose estimation"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "segmentation driven 6d object pose estimation"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "deep neural networks with auxiliary model regulated gating for resilient multi modal sensor fusion"):
  with members:
    - "joint 3d proposal generation and object detection from view aggregation"
    - "sensor fusion for robot control through deep reinforcement learning"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "brain4cars car that knows before you do via sensory fusion deep learning architecture"
    - "multi view 3d object detection network for autonomous driving"
    - "fast r cnn"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-7 (induced by source paper "a gated peripheral foveal convolutional neural network for unified image aesthetic prediction"):
  with members:
    - "deep visual attention prediction"
    - "nima neural image assessment"
    - "a deep architecture for unified aesthetic prediction"
    - "learned perceptual image enhancement"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "deep aesthetic quality assessment with semantic information"
    - "squeeze and excitation networks"
    - "top down neural attention by excitation backprop"

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)