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: "universal successor features approximators"
  Abstract: The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks into simpler ones, the exchange of information between tasks, and the reuse of skills. We focus on one aspect in particular, namely the ability to generalise to unseen tasks. Parametric generalisation relies on the interpolation power of a function approximator that is given the task description as input; one of its most common form are universal value function approximators (UVFAs). Another way to generalise to new tasks is to exploit structure in the RL problem itself. Generalised policy improvement (GPI) combines solutions of previous tasks into a policy for the unseen task; this relies on instantaneous policy evaluation of old policies under the new reward function, which is made possible through successor features (SFs). Our proposed universal successor features approximators (USFAs) combine the advantages of all of these, namely the scalability of UVFAs, the instant inference of SFs, and the strong generalisation of GPI. We discuss the challenges involved in training a USFA, its generalisation properties and demonstrate its practical benefits and transfer abilities on a large-scale domain in which the agent has to navigate in a first-person perspective three-dimensional environment.

Connected through hyperedge HE-1 (induced by source paper "zero shot task adaptation by homoiconic meta mapping"):
  with members:
    - "lgm net learning to generate matching networks for few shot learning"
    - "online meta learning"
    - "learnable embedding space for efficient neural architecture compression"
    - "zero shot task transfer"
    - "task2vec task embedding for meta learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "meta learning with latent embedding optimization"
    - "graph hypernetworks for neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "fast task inference with variational intrinsic successor features"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "model based reinforcement learning for atari"
    - "wasserstein dependency measure for representation learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "better transfer learning with inferred successor maps"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "experience replay for continual learning"
    - "universal successor representations for transfer reinforcement learning"
    - "selective experience replay for lifelong learning"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "neural episodic control"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "planning in hierarchical reinforcement learning guarantees for using local policies"):
  with members:
    - "fast task inference with variational intrinsic successor features"
    - "hierarchical reinforcement learning approximating optimal discounted tsp using local policies"
    - "feudal networks for hierarchical reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "the option critic architecture"
    - "successor features for transfer in 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.LG(Machine Learning)