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 temporal strategic relationships using generative adversarial imitation learning"
  Abstract: This paper presents a novel framework for automatic learning of complex strategies in human decision making. The task that we are interested in is to better facilitate long term planning for complex, multi-step events. We observe temporal relationships at the subtask level of expert demonstrations, and determine the different strategies employed in order to successfully complete a task. To capture the relationship between the subtasks and the overall goal, we utilise two external memory modules, one for capturing dependencies within a single expert demonstration, such as the sequential relationship among different sub tasks, and a global memory module for modelling task level characteristics such as best practice employed by different humans based on their domain expertise. Furthermore, we demonstrate how the hidden state representation of the memory can be used as a reward signal to smooth the state transitions, eradicating subtle changes. We evaluate the effectiveness of the proposed model for an autonomous highway driving application, where we demonstrate its capability to learn different expert policies and outperform state-of-the-art methods. The scope in industrial applications extends to any robotics and automation application which requires learning from complex demonstrations containing series of subtasks.

Connected through hyperedge HE-1 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "pedestrian trajectory prediction with structured memory hierarchies"

Connected through hyperedge HE-2 (induced by source paper "memory augmented deep generative models for forecasting the next shot location in tennis"):
  with members:
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "deep decision trees for discriminative dictionary learning with adversarial multi agent trajectories"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "superposed episodic and semantic memory via sparse distributed representation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "one shot imitation learning"

Connected through hyperedge HE-3 (induced by source paper "neural memory plasticity for anomaly detection"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "brain tumor type classification via capsule networks"
    - "pedestrian trajectory prediction with structured memory hierarchies"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "learning dynamic memory networks for object tracking"
    - "applications of deep learning and reinforcement learning to biological data"
    - "tree memory networks for modelling long term temporal dependencies"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-4 (induced by source paper "pedestrian trajectory prediction with structured memory hierarchies"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "efficient large scale multi modal classification"
    - "context aware trajectory prediction"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"

Connected through hyperedge HE-5 (induced by source paper "gd gan generative adversarial networks for trajectory prediction and group detection in crowds"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "interpreting neural networks to improve politeness comprehension"

Connected through hyperedge HE-6 (induced by source paper "deep decision trees for discriminative dictionary learning with adversarial multi agent trajectories"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "deep learning in neural 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)