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: "sparse interacting gaussian processes efficiency and optimality theorems of autonomous crowd navigation"
  Abstract: We study the sparsity and optimality properties of crowd navigation and find that existing techniques do not satisfy both criteria simultaneously: either they achieve optimality with a prohibitive number of samples or tractability assumptions make them fragile to catastrophe. For example, if the human and robot are modeled independently, then tractability is attained but the planner is prone to overcautious or overaggressive behavior. For sampling based motion planning of joint human-robot cost functions, for $n_t$ agents and $T$ step lookahead, $\mathcal O(2^{2n_t T})$ samples are needed for coverage of the action space. Advanced approaches statically partition the action space into free-space and then sample in those convex regions. However, if the human is \emph{moving} into free-space, then the partition is misleading and sampling is unsafe: free space will soon be occupied. We diagnose the cause of these deficiencies---optimization happens over \emph{trajectory} space---and propose a novel solution: optimize over trajectory \emph{distribution} space by using a Gaussian process (GP) basis. We exploit the "kernel trick" of GPs, where a continuum of trajectories are captured with a mean and covariance function. By using the mean and covariance as proxies for a trajectory family we reason about collective trajectory behavior without resorting to sampling. The GP basis is sparse and optimal with respect to collision avoidance and robot and crowd intention and flexibility. GP sparsity leans heavily on the insight that joint action space decomposes into free regions; however, the decomposition contains feasible solutions only if the partition is dynamically generated. We call our approach \emph{$\mathcal O(2^{n_t})$-sparse interacting Gaussian processes}.

Connected through hyperedge HE-1 (induced by source paper "relational graph learning for crowd navigation"):
  with members:
    - "lets drive driving in a crowd by learning from tree search"
    - "learning transferable cooperative behavior in multi agent teams"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "attention is all you need"
    - "vain attentional multi agent predictive modeling"
    - "a structured self attentive sentence embedding"
    - "the predictron end to end learning and planning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"):
  with members:
    - "collaborative sampling in generative adversarial networks"
    - "collaborative gan sampling"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"):
  with members:
    - "context aware trajectory prediction"
    - "human trajectory prediction using spatially aware deep attention models"
    - "orb slam a versatile and accurate monocular slam system"

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)