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: "learning to play general video games via an object embedding network"
  Abstract: Deep reinforcement learning (DRL) has proven to be an effective tool for creating general video-game AI. However most current DRL video-game agents learn end-to-end from the video-output of the game, which is superfluous for many applications and creates a number of additional problems. More importantly, directly working on pixel-based raw video data is substantially distinct from what a human player does.In this paper, we present a novel method which enables DRL agents to learn directly from object information. This is obtained via use of an object embedding network (OEN) that compresses a set of object feature vectors of different lengths into a single fixed-length unified feature vector representing the current game-state and fulfills the DRL simultaneously. We evaluate our OEN-based DRL agent by comparing to several state-of-the-art approaches on a selection of games from the GVG-AI Competition. Experimental results suggest that our object-based DRL agent yields performance comparable to that of those approaches used in our comparative study.

Connected through hyperedge HE-1 (induced by source paper "a framework for end to end learning on semantic tree structured data"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deep sets"
    - "tree to tree neural networks for program translation"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "structural attention neural networks for improved sentiment analysis"
    - "abstract syntax networks for code generation and semantic parsing"
    - "proximal policy optimization algorithms"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-2 (induced by source paper "project thyia a forever gameplayer"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "go explore a new approach for hard exploration problems"
    - "continual lifelong learning with neural networks a review"
    - "rinascimento optimising statistical forward planning agents for playing splendor"
    - "a local approach to forward model learning results on the game of life game"
    - "deep reinforcement learning for general video game ai"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-3 (induced by source paper "learning based video game development in mlp uom an overview"):
  with members:
    - "investigating human priors for playing video games"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "deploying learning materials to game content for serious education game development a case study"
    - "procedural content generation via machine learning pcgml"
    - "learning based procedural content generation"

Connected through hyperedge HE-4 (induced by source paper "sim to real transfer of robot learning with variable length inputs"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "yolov3 an incremental improvement"
    - "virtual to real learning to control in visual semantic segmentation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "investigating human priors for playing video games"
    - "deep sets"

Connected through hyperedge HE-5 (induced by source paper "general video game ai a multi track framework for evaluating agents games and content generation algorithms"):
  with members:
    - "general video game rule generation"
    - "press space to fire automatic video game tutorial generation"
    - "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"
    - "the arcade learning environment an evaluation platform for general agents"

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)