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: "cooperating with machines"
  Abstract: Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

Connected through hyperedge HE-1 (induced by source paper "multi agent reinforcement learning and human social factors in climate change mitigation"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "designing deep reinforcement learning for human parameter exploration"):
  with members:
    - "ai in the media and creative industries"
    - "a framework for understanding unintended consequences of machine learning"
    - "deep reinforcement learning"
    - "the machine learning algorithm as creative musical tool"
    - "inline co evolution between users and information presentation for data exploration"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-3 (induced by source paper "maintaining cooperation in complex social dilemmas using deep reinforcement learning"):
  with members:
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-4 (induced by source paper "social influence as intrinsic motivation for multi agent deep reinforcement learning"):
  with members:
    - "learning to share and hide intentions using information regularization"
    - "compositional obverter communication learning from raw visual input"
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "emergence of communication in an interactive world with consistent speakers"
    - "measuring collaborative emergent behavior in multi agent reinforcement learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "machine theory of mind"
    - "emergent communication through negotiation"

Connected through hyperedge HE-5 (induced by source paper "adaptive mechanism design learning to promote cooperation"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "multiagent cooperation and competition with deep reinforcement learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "the cost of stability in coalitional games"

Connected through hyperedge HE-6 (induced by source paper "emergent communication through negotiation"):
  with members:
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "multi agent cooperation and the emergence of natural language"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "prosocial learning agents solve generalized stag hunts better than selfish ones"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-8 (induced by source paper "consequentialist conditional cooperation in social dilemmas with imperfect information"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents 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.AI(Artificial Intelligence)