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: "features projections and representation change for generalized planning"
  Abstract: Generalized planning is concerned with the characterization and computation of plans that solve many instances at once. In the standard formulation, a generalized plan is a mapping from feature or observation histories into actions, assuming that the instances share a common pool of features and actions. This assumption, however, excludes the standard relational planning domains where actions and objects change across instances. In this work, we extend the formulation of generalized planning to such domains. This is achieved by projecting the actions over the features, resulting in a common set of abstract actions which can be tested for soundness and completeness, and which can be used for generating general policies such as "if the gripper is empty, pick the clear block above x and place it on the table" that achieve the goal clear(x) in any Blocksworld instance. In this policy, "pick the clear block above x" is an abstract action that may represent the action Unstack(a, b) in one situation and the action Unstack(b, c) in another. Transformations are also introduced for computing such policies by means of fully observable non-deterministic (FOND) planners. The value of generalized representations for learning general policies is also discussed.

Connected through hyperedge HE-1 (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"
    - "learning features and abstract actions for computing generalized plans"
    - "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-2 (induced by source paper "guarantees for sound abstractions for generalized planning extended paper"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "learning features and abstract actions for computing generalized plans"
    - "first order decision diagrams for relational mdps"
    - "the automatic inference of state invariants in tim"

Connected through hyperedge HE-3 (induced by source paper "model free model based and general intelligence"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "planning with pixels in almost real time"
    - "deep learning a critical appraisal"
    - "learning generalized reactive policies using deep neural networks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-4 (induced by source paper "learning features and abstract actions for computing generalized plans"):
  with members:
    - "composable planning with attributes"
    - "representation learning on graphs methods and applications"
    - "towards deep symbolic reinforcement learning"
    - "mazebase a sandbox for learning from games"
    - "first order decision diagrams for relational mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"

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)