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: "online visual robot tracking and identification using deep lstm networks"
  Abstract: Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.

Connected through hyperedge HE-1 (induced by source paper "sdvtracker real time multi sensor association and tracking for self driving vehicles"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "robust multi modality multi object tracking"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "focal loss for dense object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-2 (induced by source paper "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions"):
  with members:
    - "grown up nimbro robots winning robocup 2017 humanoid adultsize soccer competitions"
    - "hierarchical and state based architectures for robot behavior planning and control"
    - "fused angles a representation of body orientation for balance"
    - "omnidirectional bipedal walking with direct fused angle feedback mechanisms"
    - "robocup 2016 humanoid teensize winner nimbro robust visual perception and soccer behaviors"
    - "a monocular vision system for playing soccer in low color information environments"
    - "nimbro op2x adult sized open source 3d printed humanoid robot"
    - "combining simulations and real robot experiments for bayesian optimization of bipedal gait stabilization"

Connected through hyperedge HE-3 (induced by source paper "robocup 2019 adultsize winner nimbro deep learning perception in walk kick push recovery and team play capabilities"):
  with members:
    - "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions"
    - "grown up nimbro robots winning robocup 2017 humanoid adultsize soccer competitions"
    - "location dependency in video prediction"
    - "omnidirectional bipedal walking with direct fused angle feedback mechanisms"
    - "a monocular vision system for playing soccer in low color information environments"
    - "online balanced motion generation for humanoid robots"
    - "real time visual tracking and identification for a team of homogeneous humanoid robots"
    - "nimbro op2x adult sized open source 3d printed humanoid robot"

Connected through hyperedge HE-4 (induced by source paper "detection and tracking of small objects in sparse 3d laser range data"):
  with members:
    - "fast autonomous flight in warehouses for inventory applications"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "team nimbro at mbzirc 2017 autonomous valve stem turning using a wrench"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

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)