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: "irregular repetition slotted aloha over the rayleigh block fading channel with capture"
  Abstract: Random access protocols relying on the transmission of packet replicas in multiple slots and exploiting interference cancellation at the receiver have been shown to achieve per- formance competitive with that of orthogonal schemes. So far the optimization of the repetition degree profile, defining the probability for a user to transmit a given number of replicas, has mainly been performed targeting the collision channel model. In this paper the analysis is extended to a block fading channel model, also assuming capture effect at the receiver. Density evolution equations are developed for the new setting and, based on them, some repetition degree profiles are optimized and analyzed via Monte Carlo simulation in a finite frame length setting. The derived distributions are shown to achieve throughputs largely exceeding 1 [packet/slot].

Connected through hyperedge HE-1 (induced by source paper "random noma with cross slot successive interference cancellation packet recovery"):
  with members:
    - "maximum sum rate of slotted aloha with successive interference cancellation"
    - "noma based random access with multichannel aloha"
    - "maximum sum rate of slotted aloha with capture"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "aloha random access that operates as a rateless code"

Connected through hyperedge HE-2 (induced by source paper "fixed symbol aided random access scheme for machine to machine communications"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "sparse message passing based preamble estimation for crowded m2m communications"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"

Connected through hyperedge HE-3 (induced by source paper "learn2mac online learning multiple access for urllc applications"):
  with members:
    - "complexity of urllc scheduling and efficient approximation schemes"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "on the modeling and performance assessment of random access with sic"):
  with members:
    - "error floor analysis of coded slotted aloha over packet erasure channels"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "coded random access applying codes on graphs to design random access protocols"
    - "exploiting capture effect in frameless aloha for massive wireless random access"
    - "five disruptive technology directions for 5g"

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)