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 "actor conditioned attention maps for video action detection"):
  Abstract: Interactions with surrounding objects and people contain important information towards understanding human actions. In order to model such interactions explicitly, we propose to generate attention maps that rank each spatio-temporal region's importance to a detected actor. We refer to these as Actor-Conditioned Attention Maps (ACAM), and these maps serve as weights to the features extracted from the whole scene. These resulting actor-conditioned features help focus the learned model on regions that are important/relevant to the conditioned actor. Another novelty of our approach is in the use of pre-trained object detectors, instead of region proposals, that generalize better to videos from different sources. Detailed experimental results on the AVA 2.1 datasets demonstrate the importance of interactions, with a performance improvement of 5 mAP with respect to state of the art published results.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "yh technologies at activitynet challenge 2018":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning spatio temporal representation with local and global diffusion")
  Member "a better baseline for ava":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action genome actions as composition of spatio temporal scene graphs")
  Member "actor centric relation network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "the activitynet large scale activity recognition challenge 2018 summary":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refineloc iterative refinement for weakly supervised action localization")
  Member "detecting and recognizing human object interactions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)