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 survey of knowledge representation in service robotics"
  Abstract: Abstract   Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modeling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.

Connected through hyperedge HE-1 (induced by source paper "towards a framework for visual intelligence in service robotics epistemic requirements and gap analysis"):
  with members:
    - "recent advances in natural language inference a survey of benchmarks resources and approaches"
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "knowledge is never enough towards web aided deep open world recognition"
    - "continual lifelong learning with neural networks a review"
    - "integrating knowledge and reasoning in image understanding"
    - "aser a large scale eventuality knowledge graph"
    - "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"
    - "visual semantic information pursuit a survey"

Connected through hyperedge HE-2 (induced by source paper "joint object and state recognition using language knowledge"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "joint discovery of object states and manipulation actions"
    - "functional object oriented network for manipulation learning"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "boosting image captioning with attributes"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-3 (induced by source paper "manipulation motion taxonomy and coding for robots"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "scaling egocentric vision the epic kitchens dataset"
    - "functional object oriented network construction expansion"
    - "a dataset of daily interactive manipulation"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-4 (induced by source paper "a weighted functional object oriented network for task planning"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "functional object oriented network for manipulation learning"

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)