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: "soundloc acoustic method for indoor localization without infrastructure"
  Abstract: SoundLoc: Acoustic Method for Indoor Localization without Infrastructure Ruoxi Jia University of California, Berkeley Berkeley, CA 94720 ruoxijia@berkeley.edu Ming Jin University of California, Berkeley Berkeley, CA 94720 jinming@berkeley.edu ABSTRACT Identifying locations of occupants is beneficial to energy management in buildings. A key observation in indoor environment is that distinct functional areas are typically controlled by separate HVAC and lighting systems and room level localization is sufficient to provide a powerful tool for energy usage reduction by occupancy-based actuation of the building facilities. Based upon this observation, this paper focuses on identifying the room where a person or a mobile device is physically present. Existing room localization methods, however, require special infrastructure to annotate rooms. SoundLoc is a room-level localization system that exploits the intrinsic acoustic properties of individual rooms and obviates the needs for infrastructures. As we show in the study, rooms’ acoustic properties can be characterized by Room Impulse Response (RIR). Nevertheless, obtaining precise RIRs is a time- consuming and expensive process. The main contributions of our work are the following. First, a cost-effective RIR measurement system is implemented and the Noise Adaptive Extraction of Reverberation (NAER) algorithm is developed to estimate room acoustic parameters in noisy conditions. Second, a comprehensive physical and statistical analysis of features extracted from RIRs is performed. Also, SoundLoc is evaluated using the dataset consisting of ten (10) different rooms. The overall accuracy of 97.8% achieved demonstrates the potential to be integrated into automatic mapping of building space. Categories and Subject Descriptors H.5.5 [Information Interfaces and Presentation]: Sound and Music Computing – Signal analysis, synthesis, and processing. H.4.1 [Information Systems Applications]: Office Automation General Terms Algorithms, Measurement, Performance, Design, Experimentation Keywords Indoor localization, Room acoustics, Room identification 1. INTRODUCTION Commercial buildings contribute to 19% of the primary energy consumption in US. Prior research has shown that most of buildings use static control for building facilities, such as Heating, Ventilation and Air Conditioning (HVAC) and lighting systems, thereby considerable energy is wasted in conditioning and lighting unoccupied spaces [1, 2]. Awareness of occupancy information can help adaptively run the conditioning and lighting systems and reduce energy consumption in buildings. Therefore, availability of indoor locations has become an immediate need. Unlike outdoors, where GPS can provide a relatively accurate and robust solution for positioning, indoor localization has not been equally facilitated by GPS due to significant positioning error of Costas J. Spanos University of California, Berkeley Berkeley, CA 94720 spanos@berkeley.edu satellite based navigation systems in closed environments. A variety of alternatives have been proposed for indoor operation, ranging from visual [3] to infrared [4]. There have also been extensive research works focusing on indoor localization systems based on WiFi wireless network along with WiFi enabled devices [5]. However, the density of access points has a strong influence on localization accuracy. The reported WiFi localization accuracy drops below 70% in real usage environments since access point density may be low or occupancy variations may lead to significant WiFi signal variations. Also, these techniques face certain disadvantages that special-purpose infrastructure is required. In contrast to infrastructure-based techniques, SoundLoc is supported by internal microphone and speaker on laptops or mobile phones, which are the most ubiquitous devices. A key observation that supports our work is that indoor environment is well-structured and can be organized into areas with distinct geometry and functionalities. These areas can either be open spaces without a proper boundary like hallway, or closed spaces such as offices. “Room” refers to a closed space in most cases. We notice that the control of lighting and HVAC systems are typically room-level based. Therefore, instead of notating location with physical coordinates (latitude/longitude), a room level localization is sufficient for any occupancy-based control of lighting and HVAC systems. Radio-based techniques have birth defects of confusing nearby rooms as the inference for location is based on received WiFi signal strength, which varies with time and indoor environment changes. To overcome this shortcoming, our work exploits the acoustic properties to identify a location. The acoustic effects of a space are governed by geometry and furnishings. Even though two rooms are geospatially adjacent, they can be easily distinguishable in acoustic feature space. This article describes SoundLoc, a room identification method based on the extraction of acoustic features of rooms. The concept of “room” also incorporates open spaces, which exhibit special acoustic properties that can be leveraged for localization as well. SoundLoc exploits the acoustic effects of a room on audio signals and can be freely implemented without any infrastructures. The rest of this paper is organized as follows. Section 2 describes related work. Section 3 describes the formulation of localization problems in terms of rooms’ acoustic properties and explores various acoustic features that are promising to be used for localization. Section 4 describes the experiment design. Section 5 evaluates the performance of SoundLoc. Section 6 concludes the paper. 2. RELATED WORK Indoor localization has been extensively studied. Early works tried to build an empirical RF propagation model to estimate the location by received signal strength from multiple known access

Connected through hyperedge HE-1 (induced by source paper "proximity verification based on acoustic room impulse response"):
  with members:
    - "contextual proximity detection in the face of context manipulating adversaries"
    - "sound proof usable two factor authentication based on ambient sound"

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.HC(Human-Computer Interaction)