human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "joint encoding of appearance and motion features with self supervision for first person action recognition"):
  Abstract: Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from a first-person point of view. An open challenge is how to cope with the limited amount of motion information available about the action itself, as opposed to the more investigated third-person action recognition scenario. When focusing on manipulation tasks, videos tend to record only parts of the movement, making crucial the understanding of the objects being manipulated and of their context. Previous works addressed this issue with two-stream architectures, one dedicated to modeling the appearance of objects involved in the action, another dedicated to extracting motion features from optical flow. In this paper, we argue that features from these two information channels should be learned jointly to capture the spatio-temporal correlations between the two in a better way. To this end, we propose a single stream architecture able to do so, thanks to the addition of a self-supervised block that uses a pretext motion segmentation task to intertwine motion and appearance knowledge. Experiments on several publicly available databases show the power of our approach.

Includes members:
  Member "grasp2vec learning object representations from self supervised grasping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "boosting self supervised learning via knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "unsupervised representation learning by predicting image rotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "attention is all we need nailing down object centric attention for egocentric activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "lsta long short term attention for egocentric action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "audio visual scene analysis with self supervised multisensory features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal self supervision from generalized data transformations")
  Member "representation learning by learning to count":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "online action detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")

Question: Please tell me which class the source paper 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)