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: "noise filtering channel modeling and energy utilization in wireless body area networks"
  Abstract: Constant monitoring of patients without disturbing their daily activities can be achieved through mobile networks. Sensor nodes distributed in a home environment to provide home assistance gives concept of Wireless Wearable Body Area Networks. Gathering useful information and its transmission to the required destination may face several problems. In this paper we figure out different issues and discuss their possible solutions in order to obtain an optimized infrastructure for the care of elderly people. Different channel models along with their characteristics, noise filtering in different equalization techniques, energy consumption and effect of different impairments have been discussed in our paper. The novelty of this work is that we highlighted multiple issues along with their possible solutions that a BAN infrastructure is still facing.

Connected through hyperedge HE-1 (induced by source paper "an energy efficient decoding scheme for wireless body area sensor networks"):
  with members:
    - "heer hybrid energy efficient reactive protocol for wireless sensor networks"
    - "energy efficient mac protocols in wireless body area sensor networks a survey"
    - "e horm an energy efficient hole removing mechanism in wireless sensor networks"
    - "divide and rule scheme for energy efficient routing in wireless sensor networks"
    - "density controlled divide and rule scheme for energy efficient routing in wireless sensor networks"
    - "towards lp modeling for maximizing throughput and minimizing routing delay in proactive protocols in wireless multi hop networks"
    - "srp ms a new routing protocol for delay tolerant wireless sensor networks"
    - "aid an energy efficient decoding scheme for ldpc codes in wireless body area sensor networks"

Connected through hyperedge HE-2 (induced by source paper "simple stable increased throughput multi hop protocol for link efficiency in wireless body area networks"):
  with members:
    - "ubiquitous healthcare in wireless body area networks a survey"
    - "q leach a new routing protocol for wsns"
    - "on energy efficiency and delay minimization in reactive protocols in wireless multi hop networks"
    - "analyzing delay in wireless multi hop heterogeneous body area networks"
    - "analyzing medium access techniques in wireless body area networks"
    - "m attempt a new energy efficient routing protocol in wireless body area sensor networks"
    - "identifying design requirements for wireless routing link metrics"
    - "interference and bandwidth adjusted etx in wireless multi hop networks"

Connected through hyperedge HE-3 (induced by source paper "the fame threshold based energy efficient fatigue measurement for wireless body area sensor networks using multiple sinks"):
  with members:
    - "energy efficient mac protocols in wireless body area sensor networks a survey"
    - "performance study of localization techniques in wireless body area sensor networks"
    - "adaptive reliable medium access control protocol for wireless body area networks"
    - "m attempt a new energy efficient routing protocol in wireless body area sensor networks"
    - "modeling propagation characteristics for arm motion in wireless body area sensor networks"
    - "on using multiple quality link metrics with destination sequenced distance vector protocol for wireless multi hop networks"
    - "identifying design requirements for wireless routing link metrics"
    - "interference and bandwidth adjusted etx in wireless multi hop networks"

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.NI(Networking and Internet Architecture)