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 "gimme signals discriminative signal encoding for multimodal activity recognition"):
  Abstract: We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture. Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results. We apply our method to 4 action recognition datasets containing skeleton sequences, inertial and motion capturing measurements as well as \wifi fingerprints that range up to 120 action classes. Our method defines the current best CNN-based approach on the NTU RGB+D 120 dataset, lifts the state of the art on the ARIL Wi-Fi dataset by +6.78%, improves the UTD-MHAD inertial baseline by +14.4%, the UTD-MHAD skeleton baseline by 1.13% and achieves 96.11% on the Simitate motion capturing data (80/20 split). We further demonstrate experiments on both, modality fusion on a signal level and signal reduction to prevent the representation from overloading.

Includes members:
  Member "joint activity recognition and indoor localization with wifi fingerprints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "person in wifi fine grained person perception using wifi")
  Member "an attention enhanced graph convolutional lstm network for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "ntu rgb d 120 a large scale benchmark for 3d human activity understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "skeleton image representation for 3d action recognition based on tree structure and reference joints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "mfas multimodal fusion architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural inheritance relation guided one shot layer assignment search")
  Member "simitate a hybrid imitation learning benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rlbench the robot learning benchmark learning environment")
  Member "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "feedback graph convolutional network for skeleton based action recognition")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)