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: "social influence as intrinsic motivation for multi agent deep reinforcement learning"
  Abstract: We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions that it could have taken, and computes their effect on the behavior of other agents. Actions that lead to bigger changes in other agents' behavior are considered influential and are rewarded. We show that this is equivalent to rewarding agents for having high mutual information between their actions. Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols. The influence rewards for all agents can be computed in a decentralized way by enabling agents to learn a model of other agents using deep neural networks. In contrast, key previous works on emergent communication in the MARL setting were unable to learn diverse policies in a decentralized manner and had to resort to centralized training. Consequently, the influence reward opens up a window of new opportunities for research in this area.

Connected through hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "emergent tool use from multi agent autocurricula"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "frequentist regret bounds for randomized least squares value iteration"

Connected through hyperedge HE-2 (induced by source paper "on the interaction between supervision and self play in emergent communication"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "capacity bandwidth and compositionality in emergent language learning"
    - "ease of teaching and language structure from emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "mastering emergent language learning to guide in simulated navigation"
    - "countering language drift via visual grounding"
    - "emergence of compositional language with deep generational transmission"

Connected through hyperedge HE-3 (induced by source paper "emergent communication with world models"):
  with members:
    - "biases for emergent communication in multi agent reinforcement learning"
    - "on the pitfalls of measuring emergent communication"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "learning latent dynamics for planning from pixels"
    - "compositional obverter communication learning from raw visual input"
    - "world models"

Connected through hyperedge HE-4 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "emi exploration with mutual information"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "social diversity and social preferences in mixed motive reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "on the utility of learning about humans for human ai coordination"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "on emergent communication in competitive multi agent teams"):
  with members:
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "strong and simple baselines for multimodal utterance embeddings"
    - "emergence of compositional language with deep generational transmission"
    - "multimodal language analysis with recurrent multistage fusion"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "emergence of communication in an interactive world with consistent speakers"
    - "how agents see things on visual representations in an emergent language game"

Connected through hyperedge HE-7 (induced by source paper "multi agent reinforcement learning as a computational tool for language evolution research historical context and future challenges"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "curious intrinsically motivated multi task multi goal reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-8 (induced by source paper "multi agent reinforcement learning and human social factors in climate change mitigation"):
  with members:
    - "cooperating with machines"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "affect control processes"
    - "stance and sentiment in tweets"
    - "multiagent cooperation and competition with deep reinforcement learning"

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)