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: "collecting coded coupons over overlapping generations"
  Abstract: Coding over subsets (known as generations) rather than over all content blocks in P2P distribution networks and other applications is necessary for a number of practical reasons such as computational complexity. A penalty for coding only within generations is an overall throughput reduction. It has been previously shown that allowing contiguous generations to overlap in a head-to-toe manner improves the throughput. We here propose and study a scheme, referred to as the {\it random annex code}, that creates shared packets between any two generations at random rather than only the neighboring ones. By optimizing very few design parameters, we obtain a simple scheme that outperforms both the non-overlapping and the head-to-toe overlapping schemes of comparable computational complexity, both in the expected throughput and in the rate of convergence of the probability of decoding failure to zero. We provide a practical algorithm for accurate analysis of the expected throughput of the random annex code for finite-length information. This algorithm enables us to quantify the throughput vs.computational complexity tradeoff, which is necessary for optimal selection of the scheme parameters.

Connected through hyperedge HE-1 (induced by source paper "a matroid framework for noncoherent random network communications"):
  with members:
    - "collecting coded coupons over generations"
    - "communication over finite field matrix channels"
    - "on metrics for error correction in network coding"
    - "packing and covering properties of subspace codes for error control in random linear network coding"
    - "error correcting codes in projective spaces via rank metric codes and ferrers diagrams"
    - "a rank metric approach to error control in random network coding"
    - "a decomposition theory for binary linear codes"
    - "coding for errors and erasures in random network coding"

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)