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: "tunneling neural perception and logic reasoning through abductive learning"
  Abstract: Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence. However, in current machine learning systems, the perception and reasoning modules are incompatible. Tasks requiring joint perception and reasoning ability are difficult to accomplish autonomously and still demand human intervention. Inspired by the way language experts decoded Mayan scripts by joining two abilities in an abductive manner, this paper proposes the abductive learning framework. The framework learns perception and reasoning simultaneously with the help of a trial-and-error abductive process. We present the Neural-Logical Machine as an implementation of this novel learning framework. We demonstrate that--using human-like abductive learning--the machine learns from a small set of simple hand-written equations and then generalizes well to complex equations, a feat that is beyond the capability of state-of-the-art neural network models. The abductive learning framework explores a new direction for approaching human-level learning ability.

Connected through hyperedge HE-1 (induced by source paper "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "learning explanatory rules from noisy data"
    - "robustfill neural program learning under noisy i o"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"):
  with members:
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "deepproblog neural probabilistic logic programming"
    - "learning a sat solver from single bit supervision"
    - "deeplogic towards end to end differentiable logical reasoning"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "recurrent relational networks"

Connected through hyperedge HE-3 (induced by source paper "efficient reinforcement learning with a thought game for starcraft"):
  with members:
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "relational deep reinforcement learning"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "world models"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"
    - "modular architecture for starcraft ii with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "deepproblog neural probabilistic logic programming"):
  with members:
    - "learning explanatory rules from noisy data"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "solving general arithmetic word problems"
    - "neural module networks"
    - "adam a method for stochastic optimization"
    - "a knowledge compilation map"

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)