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: "adaptive neural compilation"
  Abstract: This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target distribution of inputs. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.

Connected through hyperedge HE-1 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-2 (induced by source paper "houdini lifelong learning as program synthesis"):
  with members:
    - "progressive neural architecture search"
    - "progressive neural networks"
    - "learning to reason end to end module networks for visual question answering"
    - "learning to infer graphics programs from hand drawn images"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "differentiable programs with neural libraries"
    - "learning continuous semantic representations of symbolic expressions"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-3 (induced by source paper "leveraging grammar and reinforcement learning for neural program synthesis"):
  with members:
    - "neural program meta induction"
    - "inferring and executing programs for visual reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "a syntactic neural model for general purpose code generation"
    - "sequence to sequence learning as beam search optimization"
    - "programming with a differentiable forth interpreter"
    - "deepcoder learning to write programs"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "learning to perform local rewriting for combinatorial optimization"):
  with members:
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "learning to optimize tensor programs"
    - "can neural networks understand logical entailment"
    - "learning of human like algebraic reasoning using deep feedforward neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "reinforcement learning with deep energy based policies"
    - "deep reinforcement learning for multi resource multi machine job scheduling"

Connected through hyperedge HE-5 (induced by source paper "program synthesis through reinforcement learning guided tree search"):
  with members:
    - "synthesizing bijective lenses"
    - "deepcoder learning to write programs"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "terpret a probabilistic programming language for program induction"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-6 (induced by source paper "towards synthesizing complex programs from input output examples"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "towards string to tree neural machine translation"
    - "language to logical form with neural attention"
    - "overcoming catastrophic forgetting in neural networks"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "deepcoder learning to write programs"

Connected through hyperedge HE-7 (induced by source paper "learning explanatory rules from noisy data"):
  with members:
    - "learning efficient algorithms with hierarchical attentive memory"
    - "programming with a differentiable forth interpreter"
    - "differentiable functional program interpreters"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "terpret a probabilistic programming language for program induction"
    - "neural programmer inducing latent programs with gradient descent"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning to transduce with unbounded memory"

Connected through hyperedge HE-8 (induced by source paper "learning neural programs to parse programs"):
  with members:
    - "making neural programming architectures generalize via recursion"
    - "towards string to tree neural machine translation"
    - "language to logical form with neural attention"
    - "overcoming catastrophic forgetting in neural networks"
    - "recurrent neural network grammars"
    - "deepcoder learning to write programs"
    - "learning to compose words into sentences with reinforcement learning"
    - "neuro symbolic program synthesis"

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)