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: "seek and decode random access with physical layer network coding and multiuser detection"
  Abstract: We present a novel cross layer approach to random access (RA) that combines physical-layer network coding (PLNC) with multiuser detection (MUD). PLNC and MUD are applied jointly at the physical level in order to extract any linear combination of messages experiencing a collision. The set of combinations extracted from a whole frame is then processed by the receiver to recover the original packets. A simple pre-coding stage at the transmitting terminals allows the receiver to further increase system diversity. We derive an analytical bound on the system throughput and present simulation results for the decoding at the physical level as well as several performance measures at frame level in block fading channels, namely throughput, packet loss rate and energy efficiency. The results we present are promising and suggest that a cross layer approach leveraging on the joint use of PLNC and MUD can significantly improve the performance of RA systems.

Connected through hyperedge HE-1 (induced by source paper "neural network aided decoding for physical layer network coding random access"):
  with members:
    - "frameless aloha with reliability latency guarantees"
    - "performance analysis of csma with multi packet reception the inhomogeneous case"
    - "sign compute resolve for random access"

Connected through hyperedge HE-2 (induced by source paper "prioritized random mac optimization via graph based analysis"):
  with members:
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "optimal selection of adaptive streaming representations"
    - "coded random access how coding theory helps to build random access protocols"
    - "exploiting capture effect in frameless aloha for massive wireless random access"
    - "what will 5g be"
    - "cooperative slotted aloha for multi base station systems"
    - "the throughput of slotted aloha with diversity"
    - "aloha random access that operates as a rateless code"

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)