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 "centerface joint face detection and alignment using face as point"):
  Abstract: Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power. This paper proposes a one-stage method named CenterFace to simultaneously predict facial box and landmark location with real-time speed and high accuracy. The proposed method also belongs to the anchor free category. This is achieved by: (a) learning face existing possibility by the semantic maps, (b) learning bounding box, offsets and five landmarks for each position that potentially contains a face. Specifically, the method can run in real-time on a single CPU core and 200 FPS using NVIDIA 2080TI for VGA-resolution images, and can simultaneously achieve superior accuracy (WIDER FACE Val/Test-Easy: 0.935/0.932, Medium: 0.924/0.921, Hard: 0.875/0.873 and FDDB discontinuous: 0.980, continuous: 0.732). A demo of CenterFace can be available at this https URL.

Includes members:
  Member "feature selective anchor free module for single shot object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "consistent optimization for single shot object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reppoints point set representation for object detection")
  Member "retinaface single stage dense face localisation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing face representation with unlabeled data")
  Member "lffd a light and fast face detector for edge devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "objects as points":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "bottom up object detection by grouping extreme and center points":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "openpose realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)