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 functions across many orders of magnitudes"
  Abstract: Learning non-linear functions can be hard when the magnitude of the target function is unknown beforehand, as most learning algorithms are not scale invariant. We propose an algorithm to adaptively normalize these targets. This is complementary to recent advances in input normalization. Importantly, the proposed method preserves the unnormalized outputs whenever the normalization is updated to avoid instability caused by non-stationarity. It can be combined with any learning algorithm and any non-linear function approximation, including the important special case of deep learning. We empirically validate the method in supervised learning and reinforcement learning and apply it to learning how to play Atari 2600 games. Previous work on applying deep learning to this domain relied on clipping the rewards to make learning in different games more homogeneous, but this uses the domain-specific knowledge that in these games counting rewards is often almost as informative as summing these. Using our adaptive normalization we can remove this heuristic without diminishing overall performance, and even improve performance on some games, such as Ms. Pac-Man and Centipede, on which previous methods did not perform well.

Connected through hyperedge HE-1 (induced by source paper "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "off policy actor critic with shared experience replay"
    - "transformer xl attentive language models beyond a fixed length context"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "supervised policy update for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"

Connected through hyperedge HE-2 (induced by source paper "evaluating rewards for question generation models"):
  with members:
    - "interpretation of natural language rules in conversational machine reading"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "machine comprehension by text to text neural question generation"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "policy gradient with value function approximation for collective multiagent planning"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "individual planning in agent populations exploiting anonymity and frame action hypergraphs"
    - "learning to cooperate via policy search"
    - "planning for decentralized control of multiple robots under uncertainty"

Connected through hyperedge HE-4 (induced by source paper "density estimation using real nvp"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "improving variational inference with inverse autoregressive flow"
    - "identity mappings in deep residual networks"
    - "generating images with recurrent adversarial networks"
    - "continuous deep q learning with model based acceleration"
    - "colorful image colorization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-5 (induced by source paper "exploration a study of count based exploration for deep reinforcement learning"):
  with members:
    - "exploratory gradient boosting for reinforcement learning in complex domains"
    - "deep exploration via bootstrapped dqn"
    - "asynchronous methods for deep reinforcement learning"
    - "learning values across many orders of magnitude"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "pixel recurrent neural networks"
    - "benchmarking deep reinforcement learning for continuous control"

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)