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: "semi supervised tuning from temporal coherence"
  Abstract: Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we prove the efficacy of temporal coherence for semi-supervised incremental tuning. We show that a deep architecture, just mildly trained in a supervised manner, can progressively improve its classification accuracy, if exposed to video sequences of unlabeled data. The extent to which, in some cases, a semi-supervised tuning allows to improve classification accuracy (approaching the supervised one) is somewhat surprising. A number of control experiments pointed out the fundamental role of temporal coherence.

Connected through hyperedge HE-1 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "are we ready for service robots the openloris scene datasets for lifelong slam"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "incremental classifier learning with generative adversarial networks"
    - "the limits and potentials of deep learning for robotics"
    - "don t forget there is more than forgetting new metrics for continual learning"

Connected through hyperedge HE-2 (induced by source paper "fine grained continual learning"):
  with members:
    - "are we done with object recognition the icub robot s perspective"
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the limits and potentials of deep learning for robotics"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"

Connected through hyperedge HE-3 (induced by source paper "continuous learning in single incremental task scenarios"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "growing a brain fine tuning by increasing model capacity"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "memory efficient experience replay for streaming learning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-4 (induced by source paper "core50 a new dataset and benchmark for continuous object recognition"):
  with members:
    - "progressive neural networks"
    - "encoder based lifelong learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "learning without forgetting"
    - "youtube 8m a large scale video classification benchmark"
    - "active long term memory networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "deep predictive coding networks for video prediction and unsupervised learning"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "learning a driving simulator"
    - "end to end learning for self driving cars"
    - "understanding visual concepts with continuation learning"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "deconstructing the ladder network architecture"
    - "spatio temporal video autoencoder with differentiable memory"
    - "unsupervised learning of video representations using lstms"

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)