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: "error correction for index coding with coded side information"
  Abstract: Index coding is a source coding problem in which a broadcaster seeks to meet the different demands of several users, each of whom is assumed to have some prior information on the data held by the sender. If the sender knows its clients' requests and their side-information sets, then the number of packet transmissions required to satisfy all users' demands can be greatly reduced if the data is encoded before sending. The collection of side-information indices as well as the indices of the requested data is described as an instance of the index coding with side-information (ICSI) problem. The encoding function is called the index code of the instance, and the number of transmissions employed by the code is referred to as its length. The main ICSI problem is to determine the optimal length of an index code for and instance. As this number is hard to compute, bounds approximating it are sought, as are algorithms to compute efficient index codes. Two interesting generalizations of the problem that have appeared in the literature are the subject of this work. The first of these is the case of index coding with coded side information, in which linear combinations of the source data are both requested by and held as users' side-information. The second is the introduction of error-correction in the problem, in which the broadcast channel is subject to noise. #R##N#In this paper we characterize the optimal length of a scalar or vector linear index code with coded side information (ICCSI) over a finite field in terms of a generalized min-rank and give bounds on this number based on constructions of random codes for an arbitrary instance. We furthermore consider the length of an optimal error correcting code for an instance of the ICCSI problem and obtain bounds on this number, both for the Hamming metric and for rank-metric errors. We describe decoding algorithms for both categories of errors.

Connected through hyperedge HE-1 (induced by source paper "linear codes for broadcasting with noisy side information"):
  with members:
    - "uniprior index coding"
    - "index coding with erroneous side information"
    - "the single uniprior index coding problem the single sender case and the multi sender extension"
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "optimal vector linear index codes for some symmetric side information problems"
    - "blind index coding"
    - "local graph coloring and index coding"
    - "index coding an interference alignment perspective"

Connected through hyperedge HE-2 (induced by source paper "optimal error correcting delivery scheme for an optimal coded caching scheme with small buffers"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "optimal error correcting delivery scheme for coded caching with symmetric batch prefetching"
    - "noisy broadcast networks with receiver caching"
    - "fundamental limits of caching improved bounds with coded prefetching"
    - "online coded caching"
    - "coded caching with nonuniform demands"
    - "decentralized coded caching attains order optimal memory rate tradeoff"
    - "fundamental limits of caching"

Connected through hyperedge HE-3 (induced by source paper "index coding with erroneous side information"):
  with members:
    - "capacity of coded index modulation"
    - "index codes for the gaussian broadcast channel using quadrature amplitude modulation"
    - "index coding with coded side information"
    - "blind index coding"
    - "topological interference management through index coding"
    - "an equivalence between network coding and index coding"
    - "dynamic index coding for wireless broadcast networks"
    - "error correction for index coding with side information"

Connected through hyperedge HE-4 (induced by source paper "bounding the optimal rate of the icsi and iccsi problem"):
  with members:
    - "graph theory versus minimum rank for index coding"
    - "local graph coloring and index coding"
    - "an equivalence between network coding and index coding"
    - "optimal index codes with near extreme rates"
    - "error correction for index coding with side information"
    - "on the security of index coding with side information"
    - "index coding via linear programming"
    - "on the index coding problem and its relation to network coding and matroid theory"

Connected through hyperedge HE-5 (induced by source paper "error correcting functional index codes generalized exclusive laws and graph coloring"):
  with members:
    - "wireless bidirectional relaying latin squares and graph vertex coloring"
    - "wireless network coded four way relaying using latin hyper cubes"
    - "an equivalence between network coding and index coding"
    - "optimal index codes for a class of multicast networks with receiver side information"
    - "error correction for index coding with side information"
    - "network coding for computing cut set bounds"
    - "on the index coding problem and its relation to network coding and matroid theory"
    - "broadcasting with side information"

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)