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: "generative temporal models with memory"
  Abstract: We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, express uncertainty about those unpredictable elements, and rapidly identify novel elements that may help to predict the future. To create such models, we introduce Generative Temporal Models augmented with external memory systems. They are developed within the variational inference framework, which provides both a practical training methodology and methods to gain insight into the models' operation. We show, on a range of problems with sparse, long-term temporal dependencies, that these models store information from early in a sequence, and reuse this stored information efficiently. This allows them to perform substantially better than existing models based on well-known recurrent neural networks, like LSTMs.

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 "multigrid neural memory"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "attention is all you need"
    - "neural map structured memory for deep reinforcement learning"
    - "one shot learning with memory augmented neural networks"
    - "multigrid neural architectures"
    - "asynchronous methods for deep reinforcement learning"
    - "control of memory active perception and action in minecraft"
    - "end to end memory networks"

Connected through hyperedge HE-3 (induced by source paper "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "the functional neural process"
    - "neural processes"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "structural recurrent neural network srnn for group activity analysis"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "imitation learning of factored multi agent reactive models"):
  with members:
    - "generating multi agent trajectories using programmatic weak supervision"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "fixing a broken elbo"
    - "generative models of visually grounded imagination"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "unsupervised perceptual rewards for imitation learning"

Connected through hyperedge HE-5 (induced by source paper "sequential neural processes"):
  with members:
    - "attentive neural processes"
    - "state space lstm models with particle mcmc inference"
    - "learning latent dynamics for planning from pixels"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "learning and querying fast generative models for reinforcement learning"
    - "learning models for visual 3d localization with implicit mapping"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "learning latent dynamics for planning from pixels"):
  with members:
    - "universal planning networks"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "stochastic video generation with a learned prior"
    - "learning and querying fast generative models for reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"

Connected through hyperedge HE-7 (induced by source paper "recurrent world models facilitate policy evolution"):
  with members:
    - "one big net for everything"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "visual interaction networks"
    - "attention is all you need"
    - "unsupervised learning of disentangled representations from video"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-8 (induced by source paper "temporal difference variational auto encoder"):
  with members:
    - "stochastic adversarial video prediction"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "learning awareness models"
    - "adaptive skip intervals temporal abstraction for recurrent dynamical models"
    - "time agnostic prediction predicting predictable video frames"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "a distributional perspective on reinforcement learning"

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)