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 "the fame threshold based energy efficient fatigue measurement for wireless body area sensor networks using multiple sinks"):
  Abstract: Wireless Body Area Sensor Network (WBASN) is a technology employed mainly for patient health monitoring. New research is being done to take the technology to the next level i.e. player's fatigue monitoring in sports. Muscle fatigue is the main cause of player's performance degradation. This type of fatigue can be measured by sensing the accumulation of lactic acid in muscles. Excess of lactic acid makes muscles feel lethargic. Keeping this in mind we propose a protocol THreshold based Energy-efficient FAtigue Measurement (THE-FAME) for soccer players using WBASN. In THE-FAME protocol, a composite parameter has been used that consists of a threshold parameter for lactic acid accumulation and a parameter for measuring distance covered by a particular player. When any parameters's value in this composite parameter shows an increase beyond threshold, the players is declared to be in a fatigue state. The size of battery and sensor should be very small for the sake of players' best performance. These sensor nodes, implanted inside player's body, are made energy efficient by using multiple sinks instead of a single sink. Mat lab simulation results show the effectiveness of THE-FAME.

Includes members:
  Member "energy efficient mac protocols in wireless body area sensor networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks")
  Member "performance study of localization techniques in wireless body area sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks")
  Member "noise filtering channel modeling and energy utilization in wireless body area networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks")
  Member "adaptive reliable medium access control protocol for wireless body area networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks")
  Member "m attempt a new energy efficient routing protocol in wireless body area sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distance aware relaying energy efficient dare to monitor patients in multi hop body area sensor networks")
  Member "modeling propagation characteristics for arm motion in wireless body area sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks")
  Member "on using multiple quality link metrics with destination sequenced distance vector protocol for wireless multi hop networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distance aware relaying energy efficient dare to monitor patients in multi hop body area sensor networks")
  Member "identifying design requirements for wireless routing link metrics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distance aware relaying energy efficient dare to monitor patients in multi hop body area sensor networks")

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.NI(Networking and Internet Architecture)