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: "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"
  Abstract: An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understanding and reasoning from an egocentric perspective. When trained from simulations, optimal environments should satisfy a currently unobtainable combination of high-fidelity photographic observations, massive amounts of different environment configurations and fast simulation speeds. In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations. We present a suite of tasks requiring complex reasoning and exploration in continuous, partially observable 3D environments. The objective is to provide challenging scenarios and a robust baseline agent architecture that can be trained on mid-range consumer hardware in under 24h. Our scenarios combine two key advantages: (i) they are based on a simple but highly efficient 3D environment (ViZDoom) which allows high speed simulation (12000fps); (ii) the scenarios provide the user with a range of difficulty settings, in order to identify the limitations of current state of the art algorithms and network architectures. We aim to increase accessibility to the field of Deep-RL by providing baselines for challenging scenarios where new ideas can be iterated on quickly. We argue that the community should be able to address challenging problems in reasoning of mobile agents without the need for a large compute infrastructure.

Connected through hyperedge HE-1 (induced by source paper "generalization in reinforcement learning with selective noise injection and information bottleneck"):
  with members:
    - "rogue gym a new challenge for generalization in reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "natural environment benchmarks for reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "drlviz understanding decisions and memory in deep reinforcement learning"):
  with members:
    - "learning 3d navigation protocols on touch interfaces with cooperative multi agent reinforcement learning"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "lstm a search space odyssey"
    - "deep reinforcement learning a brief survey"
    - "deep learning for video game playing"
    - "neural map structured memory for deep reinforcement learning"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"

Connected through hyperedge HE-3 (induced by source paper "egomap projective mapping and structured egocentric memory for deep rl"):
  with members:
    - "learning exploration policies for navigation"
    - "gibson env real world perception for embodied agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning to navigate in cities without a map"
    - "home a household multimodal environment"
    - "attention is all you need"
    - "neural map structured memory for deep reinforcement learning"
    - "iqa visual question answering in interactive environments"

Connected through hyperedge HE-4 (induced by source paper "learning 3d navigation protocols on touch interfaces with cooperative multi agent reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"
    - "learning to recognize touch gestures recurrent vs convolutional features and dynamic sampling"
    - "continuous control with deep reinforcement learning"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "agnostic system identification for model based 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)