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: "pomdp model learning for human robot collaboration"
  Abstract: Recent years have seen human robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing work in HRC focused on either low-level human-aware motion planning or HRC interface design, we are particularly interested in a formal design of HRC with respect to high-level complex missions, where it is of critical importance to obtain an accurate and meanwhile tractable human model. Instead of assuming the human model is given, we ask whether it is reasonable to learn human models from observed perception data, such as the gesture, eye movements, head motions of the human in concern. As our initial step, we adopt a partially observable Markov decision process (POMDP) model in this work as mounting evidences have suggested Markovian properties of human behaviors from psychology studies. In addition, POMDP provides a general modeling framework for sequential decision making where states are hidden and actions have stochastic outcomes. Distinct from the majority of POMDP model learning literature, we do not assume that the state, the transition structure or the bound of the number of states in POMDP model is given. Instead, we use a Bayesian non-parametric learning approach to decide the potential human states from data. Then we adopt an approach inspired by probably approximately correct (PAC) learning to obtain not only an estimation of the transition probability but also a confidence interval associated to the estimation. Then, the performance of applying the control policy derived from the estimated model is guaranteed to be sufficiently close to the true model. Finally, data collected from a driver-assistance test-bed are used to train the model, which illustrates the effectiveness of the proposed learning method.

Connected through hyperedge HE-1 (induced by source paper "incremental learning of discrete planning domains from continuous perceptions"):
  with members:
    - "learning plannable representations with causal infogan"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "learning in pomdps with monte carlo tree search"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning strips operators from noisy and incomplete observations"
    - "learning symbolic models of stochastic domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "vector autoregressive pomdp model learning and planning for human robot collaboration"):
  with members:
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"

Connected through hyperedge HE-3 (induced by source paper "cost bounded active classification using partially observable markov decision processes"):
  with members:
    - "barrier certificates for assured machine teaching"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "privacy verification in pomdps via barrier certificates"
    - "synthesis of surveillance strategies via belief abstraction"
    - "active classification theory and application to underwater inspection"

Connected through hyperedge HE-4 (induced by source paper "privacy verification in pomdps via barrier certificates"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "privacy preserving controller synthesis via belief abstraction"
    - "permissive finite state controllers of pomdps using parameter synthesis"
    - "value function approximations for partially observable markov decision processes"

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.HC(Human-Computer Interaction)