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: "prediction under uncertainty with error encoding networks"
  Abstract: In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling com- ponents of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimensional latent variable which is fed into the forward model. Our method uses a simple su- pervised training objective which is fast and easy to train. We evaluate it in the context of video prediction on multiple datasets and show that it is able to consi- tently generate diverse predictions without the need for alternating minimization over a latent space or adversarial training.

Connected through hyperedge HE-1 (induced by source paper "phyre a new benchmark for physical reasoning"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "habitat a platform for embodied ai research"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "probing physics knowledge using tools from developmental psychology"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-2 (induced by source paper "learning what you can do before doing anything"):
  with members:
    - "zero shot visual imitation"
    - "world models"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "unsupervised predictive memory in a goal directed agent"
    - "driving policy transfer via modularity and abstraction"
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-3 (induced by source paper "stochastic dynamics for video infilling"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "phasenet for video frame interpolation"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "time agnostic prediction predicting predictable video frames"
    - "context aware synthesis for video frame interpolation"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-4 (induced by source paper "stochastic video generation with a learned prior"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "recurrent environment simulators"

Connected through hyperedge HE-5 (induced by source paper "lmvp video predictor with leaked motion information"):
  with members:
    - "deep video generation prediction and completion of human action sequences"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "forecasting human dynamics from static images"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "dynamic filter 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.AI(Artificial Intelligence)