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: "tree memory networks for modelling long term temporal dependencies"
  Abstract: In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature, which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short term and long term sequences using its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems, aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting, and in both cases we outperform the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both long term and short term relationships efficiently via a hierarchical structure.

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 "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"):
  with members:
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "particle based pedestrian path prediction using lstm mdl models"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "context aware trajectory prediction"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (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"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "neural map structured memory for deep reinforcement learning"
    - "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-4 (induced by source paper "memory augmented recursive neural networks"):
  with members:
    - "can neural networks understand logical entailment"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "graph memory networks for molecular activity prediction"
    - "combining symbolic expressions and black box function evaluations in neural programs"
    - "making neural programming architectures generalize via recursion"
    - "learning continuous semantic representations of symbolic expressions"
    - "learning to transduce with unbounded memory"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-5 (induced by source paper "forecasting future action sequences with neural memory networks"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "when will you do what anticipating temporal occurrences of activities"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-6 (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 temporal strategic relationships using generative adversarial imitation learning"
    - "learning dynamic memory networks for object tracking"
    - "applications of deep learning and reinforcement learning to biological data"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-7 (induced by source paper "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "a deep multi level network for saliency prediction"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "pixel level domain transfer"
    - "energy based generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "learning temporal strategic relationships using generative adversarial imitation learning"):
  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"
    - "simultaneous policy learning and latent state inference for imitating driver behavior"
    - "imitating driver behavior with generative adversarial networks"
    - "neural map structured memory for deep reinforcement learning"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "third person imitation learning"
    - "infogail interpretable imitation learning from visual demonstrations"

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.LG(Machine Learning)