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: "transfer of deep reactive policies for mdp planning"
  Abstract: Domain-independent probabilistic planners input an MDP description in a factored representation language such as PPDDL or RDDL, and exploit the specifics of the representation for faster planning. Traditional algorithms operate on each problem instance independently, and good methods for transferring experience from policies of other instances of a domain to a new instance do not exist. Recently, researchers have begun exploring the use of deep reactive policies, trained via deep reinforcement learning (RL), for MDP planning domains. One advantage of deep reactive policies is that they are more amenable to transfer learning. #R##N#In this paper, we present the first domain-independent transfer algorithm for MDP planning domains expressed in an RDDL representation. Our architecture exploits the symbolic state configuration and transition function of the domain (available via RDDL) to learn a shared embedding space for states and state-action pairs for all problem instances of a domain. We then learn an RL agent in the embedding space, making a near zero-shot transfer possible, i.e., without much training on the new instance, and without using the domain simulator at all. Experiments on three different benchmark domains underscore the value of our transfer algorithm. Compared against planning from scratch, and a state-of-the-art RL transfer algorithm, our transfer solution has significantly superior learning curves.

Connected through hyperedge HE-1 (induced by source paper "generalized neural policies for relational mdps"):
  with members:
    - "size independent neural transfer for rddl planning"
    - "teacher student curriculum learning"
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "asynchronous methods for deep reinforcement learning"
    - "semi supervised classification with graph convolutional networks"
    - "towards deep symbolic reinforcement learning"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "lifelong learning with a changing action set"):
  with members:
    - "learning action representations for reinforcement learning"
    - "the natural language of actions"
    - "a sliding window algorithm for markov decision processes with arbitrarily changing rewards and transitions"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "meta gradient reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-3 (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"
    - "learning features and abstract actions for computing generalized plans"
    - "deep learning a critical appraisal"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-4 (induced by source paper "qualitative numeric planning reductions and complexity"):
  with members:
    - "compact policies for fully observable non deterministic planning as sat"
    - "features projections and representation change for generalized planning"
    - "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-5 (induced by source paper "size independent neural transfer for rddl planning"):
  with members:
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "asynchronous methods for deep reinforcement learning"
    - "value iteration networks"
    - "a theory of goal oriented mdps with dead ends"

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)