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: "learning features and abstract actions for computing generalized plans"
  Abstract: Generalized planning is concerned with the computation of plans that solve not one but multiple instances of a planning domain. Recently, it has been shown that generalized plans can be expressed as mappings of feature values into actions, and that they can often be computed with fully observable non-deterministic (FOND) planners. The actions in such plans, however, are not the actions in the instances themselves, which are not necessarily common to other instances, but abstract actions that are defined on a set of common features. The formulation assumes that the features and the abstract actions are given. In this work, we address this limitation by showing how to learn them automatically. The resulting account of generalized planning combines learning and planning in a novel way: a learner, based on a Max SAT formulation, yields the features and abstract actions from sampled state transitions, and a FOND planner uses this information, suitably transformed, to produce the general plans. Correctness guarantees are given and experimental results on several domains are reported.

Connected through hyperedge HE-1 (induced by source paper "learning first order symbolic representations for planning from the structure of the state space"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model free model based and general intelligence"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "combined reinforcement learning via abstract representations"
    - "transfer of deep reactive policies for mdp planning"
    - "deep learning a critical appraisal"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-2 (induced by source paper "qualitative numeric planning reductions and complexity"):
  with members:
    - "transfer of deep reactive policies for mdp planning"
    - "compact policies for fully observable non deterministic planning as sat"
    - "features projections and representation change for generalized planning"
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "mazebase a sandbox for learning from games"
    - "first order decision diagrams for relational mdps"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-3 (induced by source paper "guarantees for sound abstractions for generalized planning extended paper"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "features projections and representation change for generalized planning"
    - "first order decision diagrams for relational mdps"
    - "the automatic inference of state invariants in tim"

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)