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: "a learning based optimal human robot collaboration with linear temporal logic constraints"
  Abstract: This paper considers an optimal task allocation problem for human robot collaboration in human robot systems with persistent tasks. Such human robot systems consist of human operators and intelligent robots collaborating with each other to accomplish complex tasks that cannot be done by either part alone. The system objective is to maximize the probability of successfully executing persistent tasks that are formulated as linear temporal logic specifications and minimize the average cost between consecutive visits of a particular proposition. This paper proposes to model the human robot collaboration under a framework with the composition of multiple Markov Decision Process (MDP) with possibly unknown transition probabilities, which characterizes how human cognitive states, such as human trust and fatigue, stochastically change with the robot performance. Under the unknown MDP models, an algorithm is developed to learn the model and obtain an optimal task allocation policy that minimizes the expected average cost for each task cycle and maximizes the probability of satisfying linear temporal logic constraints. Moreover, this paper shows that the difference between the optimal policy based on the learned model and that based on the underlying ground truth model can be bounded by arbitrarily small constant and large confidence level with sufficient samples. The case study of an assembly process demonstrates the effectiveness and benefits of our proposed learning based human robot collaboration.

Connected through hyperedge HE-1 (induced by source paper "transfer entropy in mdps with temporal logic specifications"):
  with members:
    - "optimal control in markov decision processes via distributed optimization"
    - "information nonanticipative rate distortion function and its applications"
    - "verification of markov decision processes using learning algorithms"
    - "optimal control of mdps with temporal logic constraints"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "pomdp model learning for human robot collaboration"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "privacy verification in pomdps via barrier certificates"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-3 (induced by source paper "permissive supervisor synthesis for markov decision processes through learning"):
  with members:
    - "transfer entropy in mdps with temporal logic specifications"
    - "permissive controller synthesis for probabilistic systems"
    - "assume guarantee abstraction refinement for probabilistic systems"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-4 (induced by source paper "distributed communication aware motion planning for multi agent systems from stl and spatel specifications"):
  with members:
    - "communication aware motion planning for multi agent systems from signal temporal logic specifications"
    - "multi agent planning under local ltl specifications and event based synchronization"
    - "robotic swarm control from spatio temporal specifications"

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.RO(Robotics)