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: "channel coded collision resolution by exploiting symbol misalignment"
  Abstract: In random-access networks, such as the IEEE 802.11 network, different users may transmit their packets simultaneously, resulting in packet collisions. Traditionally, the collided packets are simply discarded. To improve performance, advanced signal processing techniques can be applied to extract the individual packets from the collided signals. Prior work of ours has shown that the symbol misalignment among the collided packets can be exploited to improve the likelihood of successfully extracting the individual packets. However, the failure rate is still unacceptably high. This paper investigates how channel coding can be used to reduce the failure rate. We propose and investigate a decoding scheme that incorporates the exploitation of the aforementioned symbol misalignment into the channel decoding process. This is a fine-grained integration at the symbol level. In particular, collision resolution and channel decoding are applied in an integrated manner. Simulation results indicate that our method outperforms other schemes, including the straightforward method in which collision resolution and channel coding are applied separately.

Connected through hyperedge HE-1 (induced by source paper "differential distributed space time coding with imperfect synchronization in frequency selective channels"):
  with members:
    - "differential distributed space time coding with imperfect synchronization"
    - "asynchronous physical layer network coding"
    - "optimal decoding algorithm for asynchronous physical layer network coding"
    - "distributed beamforming in wireless multiuser relay interference networks with quantized feedback"
    - "ofdm based distributed space time coding for asynchronous relay networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "collision resolution by exploiting symbol misalignment"
    - "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.IT(Information Theory)