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: "deep active object recognition by joint label and action prediction"
  Abstract: An active object recognition system has the advantage of being able to act in the environment to capture images that are more suited for training and that lead to better performance at test time. In this paper, we propose a deep convolutional neural network for active object recognition that simultaneously predicts the object label, and selects the next action to perform on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time.

Connected through hyperedge HE-1 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning attention selection for person re identification"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "learning deep embeddings with histogram loss"
    - "end to end deep learning for person search"
    - "self critical sequence training for image captioning"
    - "hierarchical object detection with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "multi shot pedestrian re identification via sequential decision making"):
  with members:
    - "learning policies for adaptive tracking with deep feature cascades"
    - "quality aware network for set to set recognition"
    - "person re identification by deep joint learning of multi loss classification"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "continuous adaptation of multi camera person identification models through sparse non redundant representative selection"
    - "deep feature learning with relative distance comparison for person re identification"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "belief tree search for active object recognition"):
  with members:
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "recurrent attention models for depth based person identification"
    - "asynchronous methods for deep reinforcement learning"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "deep residual learning for image recognition"
    - "recurrent models of visual attention"
    - "multiple object recognition with visual attention"
    - "active sequential hypothesis testing"

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.AI(Artificial Intelligence)