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: "contingency aware exploration in reinforcement learning"
  Abstract: This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an attentive dynamics model (ADM) that discovers controllable elements of the observations, which are often associated with the location of the character in Atari games. The ADM is trained in a self-supervised fashion to predict the actions taken by the agent. The learned contingency information is used as a part of the state representation for exploration purposes. We demonstrate that combining actor-critic algorithm with count-based exploration using our representation achieves impressive results on a set of notoriously challenging Atari games due to sparse rewards. For example, we report a state-of-the-art score of >11,000 points on Montezuma's Revenge without using expert demonstrations, explicit high-level information (e.g., RAM states), or supervisory data. Our experiments confirm that contingency-awareness is indeed an extremely powerful concept for tackling exploration problems in reinforcement learning and opens up interesting research questions for further investigations.

Connected through hyperedge HE-1 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "torchbeast a pytorch platform for distributed rl"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "unsupervised discovery of decision states for transfer in reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "never give up learning directed exploration strategies"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta gradient reinforcement learning"
    - "an introduction to deep reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"

Connected through hyperedge HE-3 (induced by source paper "optimistic exploration even with a pessimistic initialisation"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "model based active exploration"
    - "is q learning provably efficient"

Connected through hyperedge HE-4 (induced by source paper "disentangling controllable object through video prediction improves visual reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "independently controllable features"
    - "curiosity driven exploration by self supervised prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-5 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-6 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

Connected through hyperedge HE-7 (induced by source paper "hypothesis driven skill discovery for hierarchical deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "monet unsupervised scene decomposition and representation"
    - "model based reinforcement learning for atari"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "multi object representation learning with iterative variational inference"
    - "exploration by random network distillation"
    - "learning real world robot policies by dreaming"
    - "relational deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "generative adversarial self imitation learning"
    - "exploration by random network distillation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

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)