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: "unsupervised feature learning from temporal data"
  Abstract: Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Connected through hyperedge HE-1 (induced by source paper "time contrastive learning based deep bottleneck features for text dependent speaker verification"):
  with members:
    - "end to end neural segmental models for speech recognition"
    - "end to end attention based text dependent speaker verification"
    - "adversarial network bottleneck features for noise robust speaker verification"
    - "sequence prediction with neural segmental models"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "end to end training approaches for discriminative segmental models"
    - "understanding deep learning requires rethinking generalization"
    - "incorporating pass phrase dependent background models for text dependent speaker verification"

Connected through hyperedge HE-2 (induced by source paper "an unsupervised feature learning approach to reduce false alarm rate in icus"):
  with members:
    - "a shapley value solution to game theoretic based feature reduction in false alarm detection"

Connected through hyperedge HE-3 (induced by source paper "gradient based training of slow feature analysis by differentiable approximate whitening"):
  with members:
    - "spectral inference networks unifying spectral methods with deep learning"
    - "unsupervised learning of semantic audio representations"
    - "improved graph based sfa information preservation complements the slowness principle"
    - "xception deep learning with depthwise separable convolutions"
    - "unsupervised learning of visual representations using videos"
    - "slow and steady feature analysis higher order temporal coherence in video"
    - "learning to linearize under uncertainty"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "learning sight from sound ambient sound provides supervision for visual learning"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "multi task self supervised visual learning"
    - "look listen and learn"
    - "ambient sound provides supervision for visual learning"
    - "learning features by watching objects move"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "soundnet learning sound representations from unlabeled video"
    - "colorful image colorization"

Connected through hyperedge HE-5 (induced by source paper "curiosity driven exploration by self supervised prediction"):
  with members:
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"):
  with members:
    - "attentionnet aggregating weak directions for accurate object detection"
    - "show attend and tell neural image caption generation with visual attention"
    - "action recognition using visual attention"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "what makes imagenet good for transfer learning"):
  with members:
    - "learning representations for automatic colorization"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "learning without forgetting"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"
    - "instance aware semantic segmentation via multi task network cascades"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "convolutional patch representations for image retrieval an unsupervised approach"):
  with members:
    - "learning image representations equivariant to ego motion"
    - "computing the stereo matching cost with a convolutional neural network"
    - "learning image representations tied to ego motion"
    - "learning to see by moving"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "exploiting local features from deep networks for image retrieval"
    - "learning to compare image patches via convolutional neural networks"
    - "aggregating deep convolutional features for image retrieval"

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)