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: "dota 2 with large scale deep reinforcement learning"
  Abstract: On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.

Connected through hyperedge HE-1 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning"):
  with members:
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-2 (induced by source paper "finding online neural update rules by learning to remember"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "large scale adversarial representation learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "metalearned neural memory"
    - "does learning require memorization a short tale about a long tail"
    - "network of evolvable neural units evolving to learn at a synaptic level"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-3 (induced by source paper "enhanced poet open ended reinforcement learning through unbounded invention of learning challenges and their solutions"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "alphastar an evolutionary computation perspective"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "taylor expansion policy optimization"):
  with members:
    - "adaptive trade offs in off policy learning"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "policy optimization through approximated importance sampling"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "policy optimization via importance sampling"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "on the power of curriculum learning in training deep networks"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation"
    - "zpd teaching strategies for deep reinforcement learning from demonstrations"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "quantifying generalization in reinforcement learning"
    - "power allocation in multi user cellular networks with deep q learning approach"

Connected through hyperedge HE-6 (induced by source paper "formulazero distributionally robust online adaptation via offline population synthesis"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "alphastar an evolutionary computation perspective"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "online control with adversarial disturbances"
    - "efficient black box assessment of autonomous vehicle safety"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "jointly learnable behavior and trajectory planning for self driving vehicles"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-7 (induced by source paper "deep adversarial reinforcement learning for object disentangling"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "risk averse robust adversarial reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "adversarial attacks on neural network policies"
    - "automatic goal generation for reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "real world human robot collaborative reinforcement learning"):
  with members:
    - "human visual attention prediction boosts learning performance of autonomous driving agents"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shared autonomy via deep reinforcement learning"
    - "machine theory of mind"
    - "learning to walk via 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)