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: "regret minimization for partially observable deep reinforcement learning"
  Abstract: Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial or non-Markovian observations by using finite-length frame-history observations or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to a cumulative clipped advantage function and is robust to partially observed state. We demonstrate that on several partially observed reinforcement learning tasks, this new class of algorithms can substantially outperform strong baseline methods: on Pong with single-frame observations, and on the challenging Doom (ViZDoom) and Minecraft (Malm\"o) first-person navigation benchmarks.

Connected through hyperedge HE-1 (induced by source paper "combining no regret and q learning"):
  with members:
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "stable predictive optimistic counterfactual regret minimization"
    - "learning to collaborate in markov decision processes"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"

Connected through hyperedge HE-2 (induced by source paper "single deep counterfactual regret minimization"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "solving imperfect information games via discounted regret minimization"
    - "depth limited solving for imperfect information games"
    - "deep counterfactual regret minimization"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "safe and nested subgame solving for imperfect information games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-3 (induced by source paper "active perception in adversarial scenarios using maximum entropy deep reinforcement learning"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "reinforcement learning with deep energy based policies"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "qmdp net deep learning for planning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-4 (induced by source paper "inducing cooperation via team regret minimization based multi agent deep reinforcement learning"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "bayesian reinforcement learning in factored pomdps"
    - "actor attention critic for multi agent reinforcement learning"
    - "deep counterfactual regret minimization"

Connected through hyperedge HE-5 (induced by source paper "actor critic policy optimization in partially observable multiagent environments"):
  with members:
    - "combining no regret and q learning"
    - "introduction to online convex optimization"
    - "policy gradient with value function approximation for collective multiagent planning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "a generalised method for empirical game theoretic analysis"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "expert augmented actor critic for vizdoom and montezumas revenge"):
  with members:
    - "pretraining deep actor critic reinforcement learning algorithms with expert demonstrations"
    - "playing hard exploration games by watching youtube"
    - "td or not td analyzing the role of temporal differencing in deep reinforcement learning"
    - "observe and look further achieving consistent performance on atari"
    - "learning montezuma s revenge from a single demonstration"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"

Connected through hyperedge HE-7 (induced by source paper "deep counterfactual regret minimization"):
  with members:
    - "single deep counterfactual regret minimization"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "depth limited solving for imperfect information games"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"

Connected through hyperedge HE-8 (induced by source paper "memory based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge"):
  with members:
    - "j mod 2 joint monocular obstacle detection and depth estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "unsupervised learning of depth and ego motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "temporal attention gated model for robust sequence classification"
    - "deeper depth prediction with fully convolutional residual networks"

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)