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: "differentiable functional program interpreters"
  Abstract: Programming by Example (PBE) is the task of inducing computer programs from input-output examples. It can be seen as a type of machine learning where the hypothesis space is the set of legal programs in some programming language. Recent work on differentiable interpreters relaxes the discrete space of programs into a continuous space so that search over programs can be performed using gradient-based optimization. While conceptually powerful, so far differentiable interpreter-based program synthesis has only been capable of solving very simple problems. In this work, we study modeling choices that arise when constructing a differentiable programming language and their impact on the success of synthesis. The main motivation for the modeling choices comes from functional programming: we study the effect of memory allocation schemes, immutable data, type systems, and built-in control-flow structures. Empirically we show that incorporating functional programming ideas into differentiable programming languages allows us to learn much more complex programs than is possible with existing differentiable languages.

Connected through hyperedge HE-1 (induced by source paper "learning programmatically structured representations with perceptor gradients"):
  with members:
    - "towards symbolic reinforcement learning with common sense"
    - "isolating sources of disentanglement in variational autoencoders"
    - "programmatically interpretable reinforcement learning"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "deep reinforcement learning a brief survey"
    - "learning to infer graphics programs from hand drawn images"
    - "using program induction to interpret transition system dynamics"
    - "beating atari with natural language guided reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "explaining transition systems through program induction"):
  with members:
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "rationalization a neural machine translation approach to generating natural language explanations"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "a syntactic neural model for general purpose code generation"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "graying the black box understanding dqns"

Connected through hyperedge HE-3 (induced by source paper "learning explanatory rules from noisy data"):
  with members:
    - "learning efficient algorithms with hierarchical attentive memory"
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "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"

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.PL(Programming Languages)