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 detection with diverse proposals"
  Abstract: To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures. Most modern object detection architectures, such as Faster R-CNN, learn to localize objects by minimizing deviations from the ground-truth but ignore correlation between multiple proposals and object categories. Non-Maximum Suppression (NMS) as a widely used proposal pruning scheme ignores label- and instance-level relations between object candidates resulting in multi-labeled detections. In the multi-class case, NMS selects boxes with the largest prediction scores ignoring the semantic relation between categories of potential election. In contrast, our trainable DPP layer, allowing for Learning Detection with Diverse Proposals (LDDP), considers both label-level contextual information and spatial layout relationships between proposals without increasing the number of parameters of the network, and thus improves location and category specifications of final detected bounding boxes substantially during both training and inference schemes. Furthermore, we show that LDDP keeps it superiority over Faster R-CNN even if the number of proposals generated by LDPP is only ~30% as many as those for Faster R-CNN.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "grip graph based interaction aware trajectory prediction"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "dual graph convolutional network for semantic segmentation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "diverse trajectory forecasting with determinantal point processes"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "creating capsule wardrobes from fashion images"
    - "deep representation learning for human motion prediction and classification"
    - "future person localization in first person videos"

Connected through hyperedge HE-4 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"
    - "mutual information and diverse decoding improve neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "learning instance aware object detection using determinantal point processes"):
  with members:
    - "structure inference net object detection using scene level context and instance level relationships"
    - "attention is all you need"
    - "an implementation of faster rcnn with study for region sampling"
    - "relation networks for object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "learning to count objects in natural images for visual question answering"):
  with members:
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "structured attention networks"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "count ception counting by fully convolutional redundant counting"
    - "a simple neural network module for relational reasoning"
    - "learning non maximum suppression"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "structured attentions for visual question answering"

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)