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: "generic strategies for chemical space exploration"
  Abstract: Computational approaches to exploring "chemical universes", i.e., very large sets, potentially infinite sets of compounds that can be constructed by a prescribed collection of reaction mechanisms, in practice suffer from a combinatorial explosion. It quickly becomes impossible to test, for all pairs of compounds in a rapidly growing network, whether they can react with each other. More sophisticated and efficient strategies are therefore required to construct very large chemical reaction networks. #R##N#Undirected labeled graphs and graph rewriting are natural models of chemical compounds and chemical reactions. Borrowing the idea of partial evaluation from functional programming, we introduce partial applications of rewrite rules. Binding substrate to rules increases the number of rules but drastically prunes the substrate sets to which it might match, resulting in dramatically reduced resource requirements. At the same time, exploration strategies can be guided, e.g. based on restrictions on the product molecules to avoid the explicit enumeration of very unlikely compounds. To this end we introduce here a generic framework for the specification of exploration strategies in graph-rewriting systems. Using key examples of complex chemical networks from sugar chemistry and the realm of metabolic networks we demonstrate the feasibility of a high-level strategy framework. #R##N#The ideas presented here can not only be used for a strategy-based chemical space exploration that has close correspondence of experimental results, but are much more general. In particular, the framework can be used to emulate higher-level transformation models such as illustrated in a small puzzle game.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a graph based tool to embed the π calculus into a computational dpo framework"):
  with members:
    - "better late than never a fully abstract semantics for classical processes"
    - "a generic framework for engineering graph canonization algorithms"
    - "a software package for chemically inspired graph transformation"

Connected through hyperedge HE-3 (induced by source paper "planning chemical syntheses with deep neural networks and symbolic ai"):
  with members:
    - "end to end differentiable proving"
    - "modelling chemical reasoning to predict and invent reactions"
    - "monte carlo tableau proof search"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "building machines that learn and think like people"
    - "theano a python framework for fast computation of mathematical expressions"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "towards alphachem chemical synthesis planning with tree search and deep neural network policies"):
  with members:
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "training very deep networks"
    - "move evaluation in go using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "a software package for chemically inspired graph transformation"):
  with members:
    - "the graph grammar library a generic framework for chemical graph rewrite systems"
    - "inferring chemical reaction patterns using rule composition in graph grammars"
    - "porgy strategy driven interactive transformation of graphs"

Connected through hyperedge HE-6 (induced by source paper "the graph grammar library a generic framework for chemical graph rewrite systems"):
  with members:
    - "subgraph isomorphism in planar graphs and related problems"

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.FL(Formal Languages and Automata Theory)