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: "discovery of useful questions as auxiliary tasks"
  Abstract: Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this departs from the focus in much of machine learning on agents learning answers to externally defined questions. We present a novel method for a reinforcement learning (RL) agent to discover questions formulated as general value functions or GVFs, a fairly rich form of knowledge representation. Specifically, our method uses non-myopic meta-gradients to learn GVF-questions such that learning answers to them, as an auxiliary task, induces useful representations for the main task faced by the RL agent. We demonstrate that auxiliary tasks based on the discovered GVFs are sufficient, on their own, to build representations that support main task learning, and that they do so better than popular hand-designed auxiliary tasks from the literature. Furthermore, we show, in the context of Atari 2600 videogames, how such auxiliary tasks, meta-learned alongside the main task, can improve the data efficiency of an actor-critic agent.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "world discovery models"
    - "weight agnostic neural networks"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "vision language navigation with self supervised auxiliary reasoning tasks"):
  with members:
    - "test time training for out of distribution generalization"
    - "vision and dialog navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "fusion of detected objects in text for visual question answering"
    - "scene graph generation with external knowledge and image reconstruction"
    - "self supervised generalisation with meta auxiliary learning"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-3 (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"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "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.AI(Artificial Intelligence)