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: "generalized value iteration networks life beyond lattices"
  Abstract: In this paper, we introduce a generalized value iteration network (GVIN), which is an end-to-end neural network planning module. GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. We propose three novel differentiable kernels as graph convolution operators and show that the embedding based kernel achieves the best performance. We further propose episodic Q-learning, an improvement upon traditional n-step Q-learning that stabilizes training for networks that contain a planning module. Lastly, we evaluate GVIN on planning problems in 2D mazes, irregular graphs, and real-world street networks, showing that GVIN generalizes well for both arbitrary graphs and unseen graphs of larger scale and outperforms a naive generalization of VIN (discretizing a spatial graph into a 2D image).

Connected through hyperedge HE-1 (induced by source paper "value iteration networks on multiple levels of abstraction"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "planning hybrid driving stepping locomotion on multiple levels of abstraction"
    - "gated path planning networks"
    - "supervised autonomous locomotion and manipulation for disaster response with a centaur like robot"
    - "learning generalized reactive policies using deep neural networks"
    - "anytime hybrid driving stepping locomotion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-2 (induced by source paper "asnets deep learning for generalised planning"):
  with members:
    - "model free model based and general intelligence"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "the mythos of model interpretability"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "value propagation networks"):
  with members:
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "cooperative motion planning for non holonomic agents with value iteration networks"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "learning generalized reactive policies using deep neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "value prediction network"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "neural slam learning to explore with external memory"

Connected through hyperedge HE-4 (induced by source paper "action schema networks generalised policies with deep learning"):
  with members:
    - "learning generalized reactive policies using deep neural networks"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "graph structured representations for visual question answering"
    - "value iteration networks"
    - "structural rnn deep learning on spatio temporal graphs"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

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)