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: "ecat event capture annotation tool"
  Abstract: This paper introduces the Event Capture Annotation Tool (ECAT), a user-friendly, open-source interface tool for annotating events and their participants in video, capable of extracting the 3D positions and orientations of objects in video captured by Microsoft's Kinect(R) hardware. The modeling language VoxML (Pustejovsky and Krishnaswamy, 2016) underlies ECAT's object, program, and attribute representations, although ECAT uses its own spec for explicit labeling of motion instances. The demonstration will show the tool's workflow and the options available for capturing event-participant relations and browsing visual data. Mapping ECAT's output to VoxML will also be addressed.

Connected through hyperedge HE-1 (induced by source paper "multimodal interactive learning of primitive actions"):
  with members:
    - "universal semantic parsing"
    - "semantics derived automatically from language corpora contain human like biases"
    - "unsupervised human action detection by action matching"
    - "unsupervised learning from narrated instruction videos"

Connected through hyperedge HE-2 (induced by source paper "learning event representation as sparse as possible but not sparser"):
  with members:
    - "fine grained event learning of human object interaction with lstm crf"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "deep learning in neural networks"
    - "bidirectional lstm crf models for sequence tagging"
    - "qtc3d extending the qualitative trajectory calculus to three dimensions"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "fine grained event learning of human object interaction with lstm crf"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "deep learning in neural networks"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "multimodal semantic simulations of linguistically underspecified motion events"):
  with members:
    - "fine grained event learning of human object interaction with lstm crf"
    - "voxml a visualization modeling language"
    - "globally normalized transition based neural networks"
    - "generating simulations of motion events from verbal descriptions"
    - "text to 3d scene generation with rich lexical grounding"
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

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.CL(Computation and Language)