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 "unsupervised learning of latent physical properties using perception prediction networks"):
  Abstract: We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN). Consisting of a perception module that extracts representations of latent object properties and a prediction module that uses those extracted properties to simulate system dynamics, the PPN can be trained in an end-to-end fashion purely from samples of object dynamics. The representations of latent object properties learned by PPNs not only are sufficient to accurately simulate the dynamics of systems comprised of previously unseen objects, but also can be translated directly into human-interpretable properties (e.g., mass, coefficient of restitution) in an entirely unsupervised manner. Crucially, PPNs also generalize to novel scenarios: their gradient-based training can be applied to many dynamical systems and their graph-based structure functions over systems comprised of different numbers of objects. Our results demonstrate the efficacy of graph-based neural architectures in object-centric inference and prediction tasks, and our model has the potential to discover relevant object properties in systems that are not yet well understood.

Includes members:
  Member "flexible neural representation for physics prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "learning a physical long term predictor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "visual interaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "discovering objects and their relations from entangled scene representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural consciousness flow")
  Member "gated graph sequence neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "learning physical intuition of block towers by example":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "a compositional object based approach to learning physical dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "newtonian image understanding unfolding the dynamics of objects in static images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predicting the physical dynamics of unseen 3d objects")

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.LG(Machine Learning)