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: "the effects of negative adaptation in model agnostic meta learning"
  Abstract: The capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key factor in the recent success of these methods on few-shot learning problems. This particular advantage of using meta-learning over standard supervised or reinforcement learning is only well founded under the assumption that the adaptation phase does improve the performance of our model on the task of interest. However, in the classical framework of meta-learning, this constraint is only mildly enforced, if not at all, and we only see an improvement on average over a distribution of tasks. In this paper, we show that the adaptation in an algorithm like MAML can significantly decrease the performance of an agent in a meta-reinforcement learning setting, even on a range of meta-training tasks.

Connected through hyperedge HE-1 (induced by source paper "curriculum in gradient based meta reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "mame model agnostic meta exploration"
    - "learning domain randomization distributions for transfer of locomotion policies"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

Connected through hyperedge HE-2 (induced by source paper "online gradient based mixtures for transfer modulation in meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "probabilistic model agnostic meta learning"
    - "metalearning with hebbian fast weights"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-3 (induced by source paper "reconciling meta learning and continual learning with online mixtures of tasks"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "probabilistic model agnostic meta learning"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "a model of inductive bias 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)