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 programs with neural libraries"
  Abstract: We develop a framework for combining differentiable programming languages with neural networks. Using this framework we create end-to-end trainable systems that learn to write interpretable algorithms with perceptual components. We explore the benefits of inductive biases for strong generalization and modularity that come from the program-like structure of our models. In particular, modularity allows us to learn a library of (neural) functions which grows and improves as more tasks are solved. Empirically, we show that this leads to lifelong learning systems that transfer knowledge to new tasks more effectively than baselines.

Connected through hyperedge HE-1 (induced by source paper "structural language models of code"):
  with members:
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"

Connected through hyperedge HE-2 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-3 (induced by source paper "structural language models for any code generation"):
  with members:
    - "structural language models of code"
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"

Connected through hyperedge HE-4 (induced by source paper "synthesizing action sequences for modifying model decisions"):
  with members:
    - "houdini lifelong learning as program synthesis"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "axiomatic attribution for deep networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "scalable planning with tensorflow for hybrid nonlinear domains"
    - "a unified approach to interpreting model predictions"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (induced by source paper "world programs for model based learning and planning in compositional state and action spaces"):
  with members:
    - "a model to search for synthesizable molecules"
    - "challenges of real world reinforcement learning"
    - "structured agents for physical construction"
    - "learning to represent edits"
    - "generative code modeling with graphs"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "relational inductive bias for physical construction in humans and machines"
    - "leveraging grammar and reinforcement learning for neural program synthesis"

Connected through hyperedge HE-6 (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-7 (induced by source paper "from explanation to synthesis compositional program induction for learning from demonstration"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "specification patterns for robotic missions"
    - "learning programmatically structured representations with perceptor gradients"
    - "deep drone racing learning agile flight in dynamic environments"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
    - "programmatically interpretable reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-8 (induced by source paper "biology and compositionality empirical considerations for emergent communication protocols"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"
    - "inferring and executing programs for visual reasoning"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning to reason end to end module networks for visual question answering"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"

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)