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: "reuse of neural modules for general video game playing"
  Abstract: A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.

Connected through hyperedge HE-1 (induced by source paper "a review of modularization techniques in artificial neural networks"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "graph hypernetworks for neural architecture search"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "dense and diverse capsule networks making the capsules learn better"
    - "evolving deep neural networks"
    - "hierarchical semantic segmentation using modular convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning invariant feature spaces to transfer skills with reinforcement learning"):
  with members:
    - "learning transferable policies for monocular reactive mav control"
    - "sim to real robot learning from pixels with progressive nets"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "simultaneous deep transfer across domains and tasks"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "general video game ai learning from screen capture"):
  with members:
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-4 (induced by source paper "learning modular neural network policies for multi task and multi robot transfer"):
  with members:
    - "progressive neural networks"
    - "control of memory active perception and action in minecraft"
    - "deep compositional question answering with neural module networks"
    - "simultaneous deep transfer across domains and tasks"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "end to end training of deep visuomotor policies"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "neural module networks"):
  with members:
    - "grounding of textual phrases in images by reconstruction"
    - "learning to compose neural networks for question answering"
    - "exploring models and data for image question answering"
    - "neural programmer inducing latent programs with gradient descent"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning to answer questions from image using convolutional neural network"

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.NE(Neural and Evolutionary Computing)