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 "pic permutation invariant convolution for recognizing long range activities"):
  Abstract: Neural operations as convolutions, self-attention, and vector aggregation are the go-to choices for recognizing short-range actions. However, they have three limitations in modeling long-range activities. This paper presents PIC, Permutation Invariant Convolution, a novel neural layer to model the temporal structure of long-range activities. It has three desirable properties. i. Unlike standard convolution, PIC is invariant to the temporal permutations of features within its receptive field, qualifying it to model the weak temporal structures. ii. Different from vector aggregation, PIC respects local connectivity, enabling it to learn long-range temporal abstractions using cascaded layers. iii. In contrast to self-attention, PIC uses shared weights, making it more capable of detecting the most discriminant visual evidence across long and noisy videos. We study the three properties of PIC and demonstrate its effectiveness in recognizing the long-range activities of Charades, Breakfast, and MultiThumos.

Includes members:
  Member "videograph recognizing minutes long human activities in videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "large memory layers with product keys":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory based graph networks")
  Member "long term feature banks for detailed video understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "interaction aware spatio temporal pyramid attention networks for action classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep representation learning on long tailed data a learnable embedding augmentation perspective")
  Member "scaling egocentric vision the epic kitchens dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to learn words from visual scenes")
  Member "video time properties encoders and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "videograph recognizing minutes long human activities in videos")
  Member "video action transformer network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "timeception for complex action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ego topo environment affordances from egocentric video")

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)