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: "pose from action unsupervised learning of pose features based on motion"
  Abstract: Human actions are comprised of a sequence of poses. This makes videos of humans a rich and dense source of human poses. We propose an unsupervised method to learn pose features from videos that exploits a signal which is complementary to appearance and can be used as supervision: motion. The key idea is that humans go through poses in a predictable manner while performing actions. Hence, given two poses, it should be possible to model the motion that caused the change between them. We represent each of the poses as a feature in a CNN (Appearance ConvNet) and generate a motion encoding from optical flow maps using a separate CNN (Motion ConvNet). The data for this task is automatically generated allowing us to train without human supervision. We demonstrate the strength of the learned representation by finetuning the trained model for Pose Estimation on the FLIC dataset, for static image action recognition on PASCAL and for action recognition in videos on UCF101 and HMDB51.

Connected through hyperedge HE-1 (induced by source paper "exploiting motion information from unlabeled videos for static image action recognition"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised representation learning by sorting sequences"
    - "im2flow motion hallucination from static images for action recognition"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "self supervised video representation learning with odd one out networks"

Connected through hyperedge HE-2 (induced by source paper "audio visual scene analysis with self supervised multisensory features"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "voxceleb2 deep speaker recognition"
    - "the sound of pixels"
    - "deep attractor network for single microphone speaker separation"
    - "objects that sound"

Connected through hyperedge HE-3 (induced by source paper "cross and learn cross modal self supervision"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep unsupervised learning of visual similarities"
    - "colorization as a proxy task for visual understanding"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "deep multimodal representation learning from temporal data"
    - "the kinetics human action video dataset"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-4 (induced by source paper "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"):
  with members:
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "graph distillation for action detection with privileged modalities"
    - "unsupervised representation learning by sorting sequences"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "self supervised video representation learning with odd one out networks"

Connected through hyperedge HE-5 (induced by source paper "unsupervised representation learning by sorting sequences"):
  with members:
    - "learning representations for automatic colorization"
    - "ambient sound provides supervision for visual learning"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "youtube 8m a large scale video classification benchmark"
    - "context encoders feature learning by inpainting"

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)