human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generating and estimating nonverbal alphabets for situated and multimodal communications"):
  Abstract: In this paper, we discuss the formalized approach for generating and estimating symbols (and alphabets), which can be communicated by the wide range of non-verbal means based on specific user requirements (medium, priorities, type of information that needs to be conveyed). The short characterization of basic terms and parameters of such symbols (and alphabets) with approaches to generate them are given. Then the framework, experimental setup, and some machine learning methods to estimate usefulness and effectiveness of the nonverbal alphabets and systems are presented. The previous results demonstrate that usage of multimodal data sources (like wearable accelerometer, heart monitor, muscle movements sensors, braincomputer interface) along with machine learning approaches can provide the deeper understanding of the usefulness and effectiveness of such alphabets and systems for nonverbal and situated communication. The symbols (and alphabets) generated and estimated by such methods may be useful in various applications: from synthetic languages and constructed scripts to multimodal nonverbal and situated interaction between people and artificial intelligence systems through Human-Computer Interfaces, such as mouse gestures, touchpads, body gestures, eyetracking cameras, wearables, and brain-computing interfaces, especially in applications for elderly care and people with disabilities.

Includes members:
  Member "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises")
  Member "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises")
  Member "automatized generation of alphabets of symbols":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance evaluation of deep learning networks for semantic segmentation of traffic stereo pair images")
  Member "comparative performance analysis of neural networks architectures on h2o platform for various activation functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "batch size influence on performance of graphic and tensor processing units during training and inference phases")
  Member "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms")
  Member "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises")

Question: Please tell me which class the source paper 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.HC(Human-Computer Interaction)