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: "predictive positioning and quality of service ridesharing for campus mobility on demand systems"
  Abstract: Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies to properly position vehicles and allocate customers in order to maintain a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS in small-fleet MOD systems by identifying key locations which minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.

Connected through hyperedge HE-1 (induced by source paper "a probabilistic approach for demand aware ride sharing optimization"):
  with members:
    - "deeppool distributed model free algorithm for ride sharing using deep reinforcement learning"
    - "movi a model free approach to dynamic fleet management"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "adaptive caching networks with optimality guarantees"
    - "an optimization framework for online ride sharing markets"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "incremental learning of motion primitives for pedestrian trajectory prediction at intersections"):
  with members:
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "transferable pedestrian motion prediction models at intersections"
    - "real time predictive modeling and robust avoidance of pedestrians with uncertain changing intentions"

Connected through hyperedge HE-3 (induced by source paper "safe reinforcement learning with model uncertainty estimates"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "spatial uncertainty sampling for end to end control"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "stein variational policy gradient"
    - "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
    - "social attention modeling attention in human crowds"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence for long term robot autonomy a survey"):
  with members:
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "addressing challenging place recognition tasks using generative adversarial networks"
    - "4d crop monitoring spatio temporal reconstruction for agriculture"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-5 (induced by source paper "a transferable pedestrian motion prediction model for intersections with different geometries"):
  with members:
    - "car net clairvoyant attentive recurrent network"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"

Connected through hyperedge HE-6 (induced by source paper "context aware pedestrian motion prediction in urban intersections"):
  with members:
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"
    - "greedy deep dictionary learning"

Connected through hyperedge HE-7 (induced by source paper "ride sharing and dynamic networks analysis"):
  with members:
    - "optimal dynamic coverage infrastructure for large scale fleets of reconnaissance uavs"
    - "human collective intelligence as distributed bayesian inference"
    - "an analysis of issues against the adoption of dynamic carpooling"
    - "the cooperative cleaners problem in stochastic dynamic environments"
    - "incremental learning with accuracy prediction of social and individual properties from mobile phone data"
    - "static and expanding grid coverage with ant robots complexity results"

Connected through hyperedge HE-8 (induced by source paper "demand estimation and chance constrained fleet management for ride hailing"):
  with members:
    - "data driven robust taxi dispatch under demand uncertainties"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"
    - "model predictive control of autonomous mobility on demand systems"

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.MA(Multiagent Systems)