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: "options as responses grounding behavioural hierarchies in multi agent rl"
  Abstract: We propose a novel hierarchical agent architecture for multi-agent reinforcement learning with concealed information. The hierarchy is grounded in the concealed information about other players, which resolves "the chicken or the egg" nature of option discovery. We factorise the value function over a latent representation of the concealed information and then re-use this latent space to factorise the policy into options. Low-level policies (options) are trained to respond to particular states of other agents grouped by the latent representation, while the top level (meta-policy) learns to infer the latent representation from its own observation thereby to select the right option. This grounding facilitates credit assignment across the levels of hierarchy. We show that this helps generalisation---performance against a held-out set of pre-trained competitors, while training in self- or population-play---and resolution of social dilemmas in self-play.

Connected through hyperedge HE-1 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "open ended learning in symmetric zero sum games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"

Connected through hyperedge HE-2 (induced by source paper "seed rl scalable and efficient deep rl with accelerated central inference"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "torchbeast a pytorch platform for distributed rl"
    - "discovery of useful questions as auxiliary tasks"
    - "google research football a novel reinforcement learning environment"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "cross view policy learning for street navigation"
    - "neural replicator dynamics"
    - "fast task inference with variational intrinsic successor features"

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)