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: "environment probing interaction policies"
  Abstract: A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of testing environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the 'Environment-Probing' Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment's behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used policy generalization methods on novel testing environments.

Connected through hyperedge HE-1 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning manipulation under physics constraints with visual perception"
    - "single episode policy transfer in reinforcement learning"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-2 (induced by source paper "network randomization a simple technique for generalization in deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "domain randomization for active pose estimation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "playing hard exploration games by watching youtube"
    - "learning not to learn training deep neural networks with biased data"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-3 (induced by source paper "densephysnet learning dense physical object representations via multi step dynamic interactions"):
  with members:
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "interpretable intuitive physics model"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "visual interaction networks"
    - "taking visual motion prediction to new heightfields"
    - "preparing for the unknown learning a universal policy with online system identification"

Connected through hyperedge HE-4 (induced by source paper "agent probing interaction policies"):
  with members:
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "counterfactual multi agent policy gradients"
    - "emergence of grounded compositional language in multi agent populations"
    - "vain attentional multi agent predictive modeling"

Connected through hyperedge HE-5 (induced by source paper "estimating mass distribution of articulated objects through physical interaction"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning manipulation under physics constraints with visual perception"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-6 (induced by source paper "causal induction from visual observations for goal directed tasks"):
  with members:
    - "gradient based neural dag learning"
    - "dag gnn dag structure learning with graph neural networks"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "causal reasoning from meta reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning plannable representations with causal infogan"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "hindsight experience replay"

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.RO(Robotics)