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: "attribute and or grammar for joint parsing of human attributes part and pose"
  Abstract: This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and articulation of body parts, and account for the correlations between poses and attributes. The A-AOG model is an amalgamation of three traditional grammar formulations: (i) Phrase structure grammar representing the hierarchical decomposition of the human body from whole to parts; (ii) Dependency grammar modeling the geometric articulation by a kinematic graph of the body pose; and (iii) Attribute grammar accounting for the compatibility relations between different parts in the hierarchy so that their appearances follow a consistent style. The parse graph outputs human detection, pose estimation, and attribute prediction simultaneously, which are intuitive and interpretable. We conduct experiments on two tasks on two datasets, and experimental results demonstrate the advantage of joint modeling in comparison with computing poses and attributes independently. Furthermore, our model obtains better performance over existing methods for both pose estimation and attribute prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "x tom explaining with theory of mind for gaining justified human trust"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "counterfactual visual explanations"
    - "metrics for explainable ai challenges and prospects"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "localization guided learning for pedestrian attribute recognition"):
  with members:
    - "tell me where to look guided attention inference network"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "im2flow motion hallucination from static images for action recognition"
    - "deep view sensitive pedestrian attribute inference in an end to end model"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "attribute recognition from adaptive parts"

Connected through hyperedge HE-3 (induced by source paper "curriculum learning of visual attribute clusters for multi task classification"):
  with members:
    - "automated curriculum learning for neural networks"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "teacher student curriculum learning"
    - "curriculum learning for multi task classification of visual attributes"
    - "reverse curriculum generation for reinforcement learning"
    - "class rectification hard mining for imbalanced deep learning"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-4 (induced by source paper "deep view sensitive pedestrian attribute inference in an end to end model"):
  with members:
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "attribute recognition from adaptive parts"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "weakly supervised learning of mid level features for pedestrian attribute recognition and localization"
    - "contextual action recognition with r cnn"
    - "fast r cnn"
    - "learning to recognize pedestrian attribute"

Connected through hyperedge HE-5 (induced by source paper "scene centric joint parsing of cross view videos"):
  with members:
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "an efficient minibatch acceptance test for metropolis hastings"
    - "visual7w grounded question answering in images"
    - "a restricted visual turing test for deep scene and event understanding"
    - "vqa visual question answering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "cross view action modeling learning and recognition"

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)