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: "exploiting spatial invariance for scalable unsupervised object tracking"
  Abstract: The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this will allow agents to begin operating in new environments with minimal human assistance. The task of learning to discover and track objects in videos, which we call \textit{unsupervised object tracking}, has grown in prominence in recent years; however, most architectures that address it still struggle to deal with large scenes containing many objects. In the current work, we propose an architecture that scales well to the large-scene, many-object setting by employing spatially invariant computations (convolutions and spatial attention) and representations (a spatially local object specification scheme). In a series of experiments, we demonstrate a number of attractive features of our architecture; most notably, that it outperforms competing methods at tracking objects in cluttered scenes with many objects, and that it can generalize well to videos that are larger and/or contain more objects than videos encountered during training.

Connected through hyperedge HE-1 (induced by source paper "space unsupervised object oriented scene representation via spatial attention and decomposition"):
  with members:
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "monet unsupervised scene decomposition and representation"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "a simple neural network module for relational reasoning"
    - "neural expectation maximization"
    - "continuously differentiable exponential linear units"

Connected through hyperedge HE-2 (induced by source paper "scalor generative world models with scalable object representations"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning to decompose and disentangle representations for video prediction"
    - "tracking by animation unsupervised learning of multi object attentive trackers"
    - "hierarchical attentive recurrent tracking"

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.LG(Machine Learning)