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: "is the pedestrian going to cross answering by 2d pose estimation"
  Abstract: Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"

Connected through hyperedge HE-2 (induced by source paper "fast estimating pedestrian moving state based on single 2d body pose by shallow neural network"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "simple online and realtime tracking with a deep association metric"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"

Connected through hyperedge HE-3 (induced by source paper "estimating pedestrian moving state based on single 2d body pose"):
  with members:
    - "pose flow efficient online pose tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "multimodal interaction aware motion prediction for autonomous street crossing"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "context aware pedestrian motion prediction in urban intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "scene lstm a model for human trajectory prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "why did the robot cross the road learning from multi modal sensor data for autonomous road crossing"

Connected through hyperedge HE-5 (induced by source paper "autonomous vehicles that interact with pedestrians a survey of theory and practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "intent communication between autonomous vehicles and pedestrians"
    - "recognizing car fluents from video"
    - "joint attention in autonomous driving jaad"

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.CV(Computer Vision and Pattern Recognition)