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 "formulazero distributionally robust online adaptation via offline population synthesis"):
  Abstract: Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic contributions to both challenges. First, to generate a realistic, diverse set of opponents, we develop a novel method for self-play based on replica-exchange Markov chain Monte Carlo. Second, we propose a distributionally robust bandit optimization procedure that adaptively adjusts risk aversion relative to uncertainty in beliefs about opponents' behaviors. We rigorously quantify the tradeoffs in performance and robustness when approximating these computations in real-time motion-planning, and we demonstrate our methods experimentally on autonomous vehicles that achieve scaled speeds comparable to Formula One racecars.

Includes members:
  Member "dota 2 with large scale deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "adversarial policies attacking deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "alphastar an evolutionary computation perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "enhanced poet open ended reinforcement learning through unbounded invention of learning challenges and their solutions")
  Member "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios")
  Member "online control with adversarial disturbances":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improper learning for non stochastic control")
  Member "efficient black box assessment of autonomous vehicle safety":
    also connected through other hyperedges:
      (none)
  Member "human level performance in 3d multiplayer games with population based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "jointly learnable behavior and trajectory planning for self driving vehicles":
    also connected through other hyperedges:
      (none)

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.LG(Machine Learning)