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 primer on the differential calculus of 3d orientations"
  Abstract: The proper handling of 3D orientations is a central element in many optimization problems in engineering. Unfortunately many researchers and engineers struggle with the formulation of such problems and often fall back to suboptimal solutions. The existence of many different conventions further complicates this issue, especially when interfacing multiple differing implementations. This document discusses an alternative approach which makes use of a more abstract notion of 3D orientations. The relative orientation between two coordinate systems is primarily identified by the coordinate mapping it induces. This is combined with the standard exponential map in order to introduce representation-independent and minimal differentials, which are very convenient in optimization based methods.

Connected through hyperedge HE-1 (induced by source paper "codeslam learning a compact optimisable representation for dense visual slam"):
  with members:
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "demon depth and motion network for learning monocular stereo"
    - "cnn slam real time dense monocular slam with learned depth prediction"

Connected through hyperedge HE-2 (induced by source paper "mid fusion octree based object level multi instance dynamic slam"):
  with members:
    - "fusion volumetric object level slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "maskfusion real time recognition tracking and reconstruction of multiple moving objects"
    - "robust dense mapping for large scale dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "using inertial sensors for position and orientation estimation"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "magnetometer calibration using inertial sensors"
    - "towards self calibrating inertial body motion capture"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "unscented orientation estimation based on the bingham distribution"
    - "robust inference for state space models with skewed measurement noise"
    - "modeling and interpolation of the ambient magnetic field by gaussian processes"
    - "recursive estimation of orientation based on the bingham distribution"

Connected through hyperedge HE-4 (induced by source paper "manifold geometry with fast automatic derivatives and coordinate frame semantics checking in c"):
  with members:
    - "automatic differentiation in machine learning a survey"
    - "the stan math library reverse mode automatic differentiation in c"
    - "expression templates revisited a performance analysis of current methodologies"
    - "efficient expression templates for operator overloading based automatic differentiation"
    - "integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds"

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)