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: "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
  Abstract: This paper presents a novel BigEAR big data framework that employs psychological audio processing chain (PAPC) to process smartphone-based acoustic big data collected when the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer from various activities such as laughing, singing, crying, arguing, and sighing. These annotations are based on ground truth relevant for psychologists who intend to monitor/infer the social context of individuals coping with breast cancer. We pursued a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. In the state-of-the-art methods, psychologists and their team have to hear the audio recordings for making these inferences by subjective evaluations that not only are time-consuming and costly, but also demand manual data coding for thousands of audio files. The BigEAR framework automates the audio analysis. We computed the accuracy of BigEAR with respect to the ground truth obtained from a human rater. Our approach yielded overall average accuracy of 88.76% on real-world data from couples coping with breast cancer.

Connected through hyperedge HE-1 (induced by source paper "smartear smartwatch based unsupervised learning for multi modal signal analysis in opportunistic sensing framework"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"
    - "soa fog secure service oriented edge computing architecture for smart health big data analytics"
    - "fog computing in medical internet of things architecture implementation and applications"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"

Connected through hyperedge HE-2 (induced by source paper "smart fog fog computing framework for unsupervised clustering analytics in wearable internet of things"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "fog computing in medical internet of things architecture implementation and applications"
    - "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-3 (induced by source paper "fog computing in medical internet of things architecture implementation and applications"):
  with members:
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"
    - "foggis fog computing for geospatial big data analytics"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-4 (induced by source paper "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"):
  with members:
    - "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"
    - "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"
    - "sensingkit evaluating the sensor power consumption in ios devices"
    - "automatic environmental sound recognition performance versus computational cost"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-5 (induced by source paper "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"):
  with members:
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "fog data enhancing telehealth big data through fog computing"

Connected through hyperedge HE-6 (induced by source paper "comparative analysis of spatialhadoop and geospark for geospatial big data analytics"):
  with members:
    - "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"
    - "foggis fog computing for geospatial big data analytics"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"
    - "fog data enhancing telehealth big data through fog computing"
    - "the anatomy of big data computing"

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.SD(Sound)