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 from conditional distributions via dual kernel embeddings"
  Abstract: In many machine learning problems, such as policy evaluation in reinforcement learning and learning with invariance, each data point $x$ itself is a conditional distribution $p(z|x)$, and we want to learn a function $f$ which links these conditional distributions to target values $y$. The learning problem becomes very challenging when we only have limited samples or in the extreme case only one sample from each conditional distribution $p(z|x)$. Commonly used approaches either assume that $z$ is independent of $x$, or require an overwhelmingly large sample size from each conditional distribution. #R##N#To address these challenges, we propose a novel approach which reformulates the original problem into a min-max optimization problem. In the new view, we only need to deal with the kernel embedding of the joint distribution $p(z,x)$ which is easy to estimate. Furthermore, we design an efficient learning algorithm based on mirror descent stochastic approximation, and establish the sample complexity for learning from conditional distributions. Finally, numerical experiments in both synthetic and real data show that our method can significantly improve over the previous state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "gradientdice rethinking generalized offline estimation of stationary values"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "provably convergent off policy actor critic with function approximation"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "addressing function approximation error in actor critic methods"
    - "learning from conditional distributions via dual embeddings"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "dualdice behavior agnostic estimation of discounted stationary distribution corrections"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "learning dexterous in hand manipulation"
    - "neural approaches to conversational ai"
    - "more robust doubly robust off policy evaluation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "stochastic variance reduction methods for policy evaluation"

Connected through hyperedge HE-3 (induced by source paper "voting based multi agent reinforcement learning"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "applications of deep learning and reinforcement learning to biological data"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "placement optimization of uav mounted mobile base stations"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "doubly robust bias reduction in infinite horizon off policy estimation"):
  with members:
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a kernel loss for solving the bellman equation"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "representation balancing mdps for off policy policy evaluation"
    - "more robust doubly robust off policy evaluation"

Connected through hyperedge HE-5 (induced by source paper "learning to defense by learning to attack"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "on evaluating adversarial robustness"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "multi agent reinforcement learning via double averaging primal dual optimization"):
  with members:
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "diff dac distributed actor critic for multitask deep reinforcement learning"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "boosting the actor with dual critic"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "stochastic variance reduction methods for policy evaluation"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "compositional stochastic average gradient for machine learning and related applications"):
  with members:
    - "accelerated method for stochastic composition optimization with nonsmooth regularization"
    - "fast stochastic variance reduced admm for stochastic composition optimization"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "convergence of stochastic gradient descent for pca"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "nonparametric stochastic compositional gradient descent for q learning in continuous markov decision problems"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "modelling transition dynamics in mdps with rkhs embeddings"
    - "when is there a representer theorem vector versus matrix regularizers"

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)