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 "end to end recurrent multi object tracking and trajectory prediction with relational reasoning"):
  Abstract: Relational reasoning - the ability to model interactions and relations between objects - is valuable for robust multi-object tracking and pivotal for trajectory prediction. In this paper, we propose MOHART, a class-agnostic, end-to-end multi-object tracking and trajectory prediction algorithm, which explicitly accounts for permutation invariance in its relational reasoning. We explore a number of permutation invariant architectures and show that multi-headed self-attention outperforms the provided baselines and better accounts for complex physical interactions in a challenging toy experiment. We show on three real-world tracking datasets that adding relational reasoning capabilities in this way increases the tracking and trajectory prediction performance, particularly in the presence of ego-motion, occlusions, crowded scenes, and faulty sensor inputs. To the best of our knowledge, MOHART is the first fully end-to-end multi-object tracking from vision approach applied to real-world data reported in the literature.

Includes members:
  Member "what the constant velocity model can teach us about pedestrian motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformer networks for trajectory forecasting")
  Member "sophie an attentive gan for predicting paths compliant to social and physical constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "relational neural expectation maximization unsupervised discovery of objects and their interactions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "sequential attend infer repeat generative modelling of moving objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "social gan socially acceptable trajectories with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "deep sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic latent residual video prediction")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "re3 real time recurrent regression networks for visual tracking of generic objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")

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)