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 "fast estimating pedestrian moving state based on single 2d body pose by shallow neural network"):
  Abstract: Crossing or Not-Crossing (C/NC) problem is important to autonomous vehicles (AVs) to safely interact with pedestrians. However, this problem setup ignores pedestrians walking along the direction of vehicles' movement (LONG). To enhance AVs' awareness of pedestrians behavior, we make the first step towards extending C/NC to C/NC/LONG problem and recognize them based on single body pose. In contrast, previous C/NC state classification work depend on multiple poses or contextual information. Our proposed shallow neural network classifier is able to recognize these three states within a very short time. We test our it on JAAD dataset and report average 81.23% accuracy. In order to further improve the classifier's performance, we introduce a computational-efficient method, action momentum optimizer (AMO), which correct prediction based on crossing behavior pattern. And our experiment shows that classifier perform at most 11.39% better on continuous pose test with the help of it. Furthermore, this model can cooperate with different sensors and algorithms that provide 2D pedestrian body pose so that it is able to work across multiple light and weather conditions. In addition, we have created extended annotations of pose for JAAD dataset, which will be publicly released soon

Includes members:
  Member "is the pedestrian going to cross answering by 2d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "openpose realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "simple online and realtime tracking with a deep association metric":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "hand keypoint detection in single images using multiview bootstrapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "rmpe regional multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")

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)