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 strips operators from noisy and incomplete observations"
  Abstract: Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains. We propose a method which learns STRIPS action models in such domains, by decomposing the problem into first learning a transition function between states in the form of a set of classifiers, and then deriving explicit STRIPS rules from the classifiers' parameters. We evaluate our approach on simulated standard planning domains from the International Planning Competition, and show that it learns useful domain descriptions from noisy, incomplete observations.

Connected through hyperedge HE-1 (induced by source paper "strips action discovery"):
  with members:
    - "learning strips action models with classical planning"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning partially observable deterministic action models"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "unsupervised grounding of plannable first order logic representation from images"):
  with members:
    - "photo realistic blocksworld dataset"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "deepproblog neural probabilistic logic programming"
    - "relational deep reinforcement learning"
    - "logical rule induction and theory learning using neural theorem proving"
    - "relational inductive biases deep learning and graph networks"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"

Connected through hyperedge HE-3 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "dvae discrete variational autoencoders with overlapping transformations"

Connected through hyperedge HE-4 (induced by source paper "learning action models from disordered and noisy plan traces"):
  with members:
    - "learning portable representations for high level planning"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "efficient safe and probably approximately complete learning of action models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "distributed representations of sentences and documents"
    - "new inference rules for max sat"

Connected through hyperedge HE-5 (induced by source paper "incremental learning of discrete planning domains from continuous perceptions"):
  with members:
    - "learning plannable representations with causal infogan"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "pomdp model learning for human robot collaboration"
    - "learning in pomdps with monte carlo tree search"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning symbolic models of stochastic domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-6 (induced by source paper "towards stable symbol grounding with zero suppressed state autoencoder"):
  with members:
    - "learning plannable representations with causal infogan"
    - "lstm based goal recognition in latent space"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "a survey of model compression and acceleration for deep neural networks"
    - "the ff planning system fast plan generation through heuristic search"
    - "the symbol grounding problem"

Connected through hyperedge HE-7 (induced by source paper "a gentle introduction to epistemic planning the del approach"):
  with members:
    - "cooperative epistemic multi agent planning for implicit coordination"
    - "knowledge based programs as plans succinctness and the complexity of plan existence"
    - "semantic results for ontic and epistemic change"

Connected through hyperedge HE-8 (induced by source paper "interactive learning of state representation through natural language instruction and explanation"):
  with members:
    - "a joint model of language and perception for grounded attribute learning"
    - "learning partially observable deterministic action models"

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)