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 "extd extremely tiny face detector via iterative filter reuse"):
  Abstract: In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD),less than 0.1 million, as well as achieving comparable performance to deep heavy detectors. While existing multi-scale face detectors extract feature maps with different scales from a single backbone network, our method generates the feature maps by iteratively reusing a shared lightweight and shallow backbone network. This iterative sharing of the backbone network significantly reduces the number of parameters, and also provides the abstract image semantics captured from the higher stage of the network layers to the lower-level feature map. The proposed idea is employed by various model architectures and evaluated by extensive experiments. From the experiments from WIDER FACE dataset, we show that the proposed face detector can handle faces with various scale and conditions, and achieved comparable performance to the more massive face detectors that few hundreds and tens times heavier in model size and floating point operations.

Includes members:
  Member "depthwise convolution is all you need for learning multiple visual domains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adafilter adaptive filter fine tuning for deep transfer learning")
  Member "thundernet towards real time generic object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bidet an efficient binarized object detector")
  Member "dfanet deep feature aggregation for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "improved selective refinement network for face detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "fishnet a versatile backbone for image region and pixel level prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pedestrian detection the elephant in the room")
  Member "dsfd dual shot face detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kpnet towards minimal face detector")
  Member "tiny dsod lightweight object detection for resource restricted usages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    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)