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: "collision resolution by exploiting symbol misalignment"
  Abstract: This paper presents CRESM, a novel collision resolution method for decoding collided packets in random-access wireless networks. In a collision, overlapping signals from several sources are received simultaneously at a receiver. CRESM exploits symbol misalignment among the overlapping signals to recover the individual packets. CRESM can be adopted in 802.11 networks without modification of the transmitter design; only a simple DSP technique is needed at the receiver to decode the overlapping signals. Our simulations indicate that CRESM has better BER performance than the simplistic Successive Interference Cancellation (SIC) technique that treats interference as noise, for almost all SNR regimes. The implication of CRESM for random-access networking is significant: in general, using CRESM to resolve collisions of up to n packets, network throughput can be boosted by more than n times if the transmitters are allowed to transmit more aggressively in the MAC protocol.

Connected through hyperedge HE-1 (induced by source paper "asynchronous physical layer network coding"):
  with members:
    - "optimal decoding algorithm for asynchronous physical layer network coding"
    - "on the bcjr algorithm for asynchronous physical layer network coding"
    - "physical layer network coding tutorial survey and beyond"
    - "implementation of physical layer network coding"
    - "channel coded collision resolution by exploiting symbol misalignment"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "physical network coding in two way wireless relay channels"

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)