human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"
  Abstract: In applications, such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This letter proposes a biomechanically inspired recurrent neural network that can predict the location and three-dimensional (3-D) articulated body pose of pedestrians in a global coordinate frame, given 3-D poses and locations estimated in prior frames with inaccuracy. The proposed network is able to predict poses and global locations for multiple pedestrians simultaneously, for pedestrians up to 45 m from the cameras (urban intersection scale). The outputs of the proposed network are full-body 3-D meshes represented in skinned multi-person linear model parameters. The proposed approach relies on a novel objective function that incorporates the periodicity of human walking (gait), the mirror symmetry of the human body, and the change of ground reaction forces in a human gait cycle. This letter presents prediction results on the PedX dataset, a large-scale, in-the-wild data set collected at real urban intersections with heavy pedestrian traffic. Results show that the proposed network can successfully learn the characteristics of pedestrian gait and produce accurate and consistent 3-D pose predictions.

Connected through hyperedge HE-1 (induced by source paper "structured prediction helps 3d human motion modelling"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "modeling human motion with quaternion based neural networks"
    - "quaternet a quaternion based recurrent model for human motion"
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-2 (induced by source paper "exploring pose priors for human pose estimation with joint angle representations"):
  with members:
    - "deephuman 3d human reconstruction from a single image"
    - "monocular total capture posing face body and hands in the wild"
    - "end to end recovery of human shape and pose"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

Question: Please tell me which class the center node 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.RO(Robotics)