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 message passing algorithm for multi agent trajectory planning"
  Abstract: We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM). Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments. We apply our method to classical challenging instances and observe that its computational requirements scale well with $p$ for several cost functionals. We also show that a specialization of our algorithm can be used for {\em local} motion planning by solving the problem of joint optimization in velocity space.

Connected through hyperedge HE-1 (induced by source paper "a distributed admm approach to non myopic path planning for multi target tracking"):
  with members:
    - "potential game based non myopic sensor network planning for multi target tracking"
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "multi agent deep reinforcement learning with extremely noisy observations"):
  with members:
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of grounded compositional language in multi agent populations"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "learning multiagent communication with backpropagation"
    - "unifying count based exploration and intrinsic motivation"
    - "multi agent cooperation and the emergence of natural language"

Connected through hyperedge HE-3 (induced by source paper "efficient projection onto the perfect phylogeny model"):
  with members:
    - "multi state perfect phylogeny mixture deconvolution and applications to cancer sequencing"
    - "proximal operators for multi agent path planning"
    - "fast and scalable inference of multi sample cancer lineages"
    - "inferring clonal evolution of tumors from single nucleotide somatic mutations"

Connected through hyperedge HE-4 (induced by source paper "parallel optimal control for cooperative automation of large scale connected vehicles via admm"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-5 (induced by source paper "the role of data driven priors in multi agent crowd trajectory estimation"):
  with members:
    - "crowded scene analysis a survey"
    - "realtime multilevel crowd tracking using reciprocal velocity obstacles"

Connected through hyperedge HE-6 (induced by source paper "proximal operators for multi agent path planning"):
  with members:
    - "an improved three weight message passing algorithm"
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

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.AI(Artificial Intelligence)