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: "disjoint multi task learning between heterogeneous human centric tasks"
  Abstract: Human behavior understanding is arguably one of the most important mid-level components in artificial intelligence. In order to efficiently make use of data, multi-task learning has been studied in diverse computer vision tasks including human behavior understanding. However, multi-task learning relies on task specific datasets and constructing such datasets can be cumbersome. It requires huge amounts of data, labeling efforts, statistical consideration etc. In this paper, we leverage existing single-task datasets for human action classification and captioning data for efficient human behavior learning. Since the data in each dataset has respective heterogeneous annotations, traditional multi-task learning is not effective in this scenario. To this end, we propose a novel alternating directional optimization method to efficiently learn from the heterogeneous data. We demonstrate the effectiveness of our model and show performance improvements on both classification and sentence retrieval tasks in comparison to the models trained on each of the single-task datasets.

Connected through hyperedge HE-1 (induced by source paper "dense relational captioning triple stream networks for relationship based captioning"):
  with members:
    - "linknet relational embedding for scene graph"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "inferring and executing programs for visual reasoning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-2 (induced by source paper "image captioning with very scarce supervised data adversarial semi supervised learning approach"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "textual explanations for self driving vehicles"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "iterative learning with open set noisy labels"
    - "joint training for neural machine translation models with monolingual data"
    - "unpaired image captioning by language pivoting"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-3 (induced by source paper "human action recognition in drone videos using a few aerial training examples"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "real world anomaly detection in surveillance videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "human motion capture using a drone"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised image to image translation networks"

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)