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: "towards a domain specific language for a scene graph based robotic world model"
  Abstract: Robot world model representations are a vital part of robotic applications. However, there is no support for such representations in model-driven engineering tool chains. This work proposes a novel Domain Specific Language (DSL) for robotic world models that are based on the Robot Scene Graph (RSG) approach. The RSG-DSL can express (a) application specific scene configurations, (b) semantic scene structures and (c) inputs and outputs for the computational entities that are loaded into an instance of a world model.

Connected through hyperedge HE-1 (induced by source paper "cosmo contextualized scene modeling with boltzmann machines"):
  with members:
    - "focal loss for dense object detection"
    - "a simple neural network module for relational reasoning"
    - "context reasoning in underwater robots using mebn"
    - "a deep incremental boltzmann machine for modeling context in robots"
    - "a learning based approach to incremental context modeling in robots"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "what is missing or wrong in the scene a hybrid deep boltzmann machine for contextualized scene modeling"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "a learning based approach to incremental context modeling in robots"):
  with members:
    - "context reasoning in underwater robots using mebn"
    - "a deep incremental boltzmann machine for modeling context in robots"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "robobrain large scale knowledge engine for robots"

Connected through hyperedge HE-3 (induced by source paper "what is missing or wrong in the scene a hybrid deep boltzmann machine for contextualized scene modeling"):
  with members:
    - "context reasoning in underwater robots using mebn"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "robobrain large scale knowledge engine for robots"
    - "contextually guided semantic labeling and search for 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "reasoning in complex environments with the selectscript declarative language"):
  with members:
    - "towards a robot perception specification language"

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)