human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "no press diplomacy modeling multi agent gameplay"):
  Abstract: Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Reliance on trust and coordination makes Diplomacy the first non-cooperative multi-agent benchmark for complex sequential social dilemmas in a rich environment. In this work, we focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players. We present DipNet, a neural-network-based policy model for No Press Diplomacy. The model was trained on a new dataset of more than 150,000 human games. Our model is trained by supervised learning (SL) from expert trajectories, which is then used to initialize a reinforcement learning (RL) agent trained through self-play. Both the SL and RL agents demonstrate state-of-the-art No Press performance by beating popular rule-based bots.

Includes members:
  Member "the hanabi challenge a new frontier for ai research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "other play for zero shot coordination")
  Member "human level performance in 3d multiplayer games with population based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "social influence as intrinsic motivation for multi agent deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "inequity aversion improves cooperation in intertemporal social dilemmas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cluster based social reinforcement learning")
  Member "emergent communication through negotiation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "learning with opponent learning awareness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "mastering chess and shogi by self play with a general reinforcement learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "multi agent reinforcement learning in sequential social dilemmas":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

Question: Please tell me which class the source paper 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)