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: "linear encodings of bounded ltl model checking"
  Abstract: We consider the problem of bounded model checking (BMC) for linear tempo- ral logic (LTL). We present several efficient encodings that have size linear in the bound. Furthermore, we show how the encodings can be extended to LTL with past operators (PLTL). The generalised encoding is still of linear size, but cannot detect minimal length counterexamples. By using the virtual unrolling technique minimal length counterexam- ples can be captured, however, the size of the encoding is quadratic in the specification. We also extend virtual unrolling to Buchi automata, enabling them to accept minimal length counterexamples. Our BMC encodings can be made incremental in order to benefit from incremental SAT technology. With fairly small modifications the incremental encoding can be further enhanced with a termination check, allowing us to prove properties with BMC. An analysis of the liveness-to-safety transformation reveals many similarities to the BMC encodings in this paper. We conduct experiments to determine the advantage of em- ploying dedicated BMC encodings for PLTL over combining more general but potentially less efficient approaches with BMC: the liveness-to-safety t with invariant checking and Buchi automata with fair cycle detection. Experiments clearly show that our new encodings improve performance of BMC con- siderably, particularly in the case of the incremental encoding, and that they are very competitive for finding bugs. Dedicated encodings seem to have an advantage over using more general methods with BMC. Using the liveness-to-safety translation with BDD-based invariant checking results in an efficient method to find shortest counterexamples that com- plements the BMC-based approach. For proving complex properties BDD-based methods still tend to perform better.

Connected through hyperedge HE-1 (induced by source paper "an abstraction free method for multi robot temporal logic optimal control synthesis"):
  with members:
    - "stylus a temporal logic optimal control synthesis algorithm for large scale multi robot systems"
    - "control of magnetic microrobot teams for temporal micromanipulation tasks"
    - "learning sampling distributions for robot motion planning"
    - "decentralized abstractions for multi agent systems under coupled constraints"
    - "sampling based temporal logic path planning"
    - "optimal multi robot path planning with ltl constraints guaranteeing correctness through synchronization"
    - "sampling based algorithms for optimal motion planning"
    - "synthesis of distributed control and communication schemes from global ltl specifications"

Connected through hyperedge HE-2 (induced by source paper "bounded model checking of max plus linear systems via predicate abstractions"):
  with members:
    - "tropical abstractions of max plus linear systems"

Connected through hyperedge HE-3 (induced by source paper "multirobot coordination with counting temporal logics"):
  with members:
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "control of magnetic microrobot teams for temporal micromanipulation tasks"
    - "control synthesis for permutation symmetric high dimensional systems with counting constraints"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "robust multi robot optimal path planning with temporal logic constraints"

Connected through hyperedge HE-4 (induced by source paper "combining neural networks and tree search for task and motion planning in challenging environments"):
  with members:
    - "end to end learning for self driving cars"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "continuous deep q learning with model based acceleration"
    - "modular multitask reinforcement learning with policy sketches"
    - "end to end learning of driving models from large scale video datasets"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "model predictive control for signal temporal logic specification"):
  with members:
    - "robust online monitoring of signal temporal logic"

Connected through hyperedge HE-6 (induced by source paper "extracting unsatisfiable cores for ltl via temporal resolution"):
  with members:
    - "computing minimal sets on propositional formulae i problems reductions"
    - "resolution proof transformation for compression and interpolation"
    - "enhancing unsatisfiable cores for ltl with information on temporal relevance"
    - "on the complexity of computing minimal unsatisfiable ltl formulas"
    - "computing small unsatisfiable cores in satisfiability modulo theories"
    - "handling conflicts in depth first search for ltl tableau to debug compliance based languages"
    - "clausal temporal resolution"

Connected through hyperedge HE-7 (induced by source paper "a deep inference system with a self dual binder which is complete for linear lambda calculus"):
  with members:
    - "communication and concurrency with logic based restriction inside a calculus of structures"
    - "linear lambda calculus with explicit substitutions as proof search in deep inference"
    - "a system of interaction and structure"
    - "a system of interaction and structure ii the need for deep inference"

Connected through hyperedge HE-8 (induced by source paper "complete elgot monads and coalgebraic resumptions"):
  with members:
    - "a coinductive calculus for asynchronous side effecting processes"
    - "general recursion via coinductive types"

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.LO(Logic in Computer Science)