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: "socialhbc social networking and secure authentication using interference robust human body communication"
  Abstract: With the advent of cheap computing through five decades of continued miniaturization following Moore's Law, wearable devices are becoming increasingly popular. These wearable devices are typically interconnected using wireless body area network (WBAN). Human body communication (HBC) provides an alternate energy-efficient communication technique between on-body wearable devices by using the human body as a conducting medium. This allows order of magnitude lower communication power, compared to WBAN, due to lower loss and broadband signaling. Moreover, HBC is significantly more secure than WBAN, as the information is contained within the human body and cannot be snooped on unless the person is physically touched. In this paper, we highlight applications of HBC as (1) Social Networking (e.g. LinkedIn/Facebook friend request sent during Handshaking in a meeting/party), (2) Secure Authentication using human-human or human-machine dynamic HBC and (3) ultra-low power, secure BAN using intra-human HBC. One of the biggest technical bottlenecks of HBC has been the interference (e.g. FM) picked up by the human body acting like an antenna. In this work for the first time, we introduce an integrating dual data rate (DDR) receiver technique, that allows notch filtering (>20 dB) of the interference for interference-robust HBC.

Connected through hyperedge HE-1 (induced by source paper "characterization and classification of human body channel as a function of excitation and termination modalities"):
  with members:
    - "wearable health monitoring using capacitive voltage mode human body communication"

Connected through hyperedge HE-2 (induced by source paper "design considerations of a sub 50 mu w receiver front end for implantable devices in medradio band"):
  with members:
    - "trifecta security energy efficiency and communication capacity comparison for wireless iot devices"

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)