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: "broadcasting with side information"
  Abstract: A sender holds a word x consisting of n blocks x_i, each of t bits, and wishes to broadcast a codeword to m receivers, R_1,...,R_m. Each receiver R_i is interested in one block, and has prior side information consisting of some subset of the other blocks. Let \beta_t be the minimum number of bits that has to be transmitted when each block is of length t, and let \beta be the limit \beta = \lim_{t \to \infty} \beta_t/t. In words, \beta is the average communication cost per bit in each block (for long blocks). Finding the coding rate \beta, for such an informed broadcast setting, generalizes several coding theoretic parameters related to Informed Source Coding on Demand, Index Coding and Network Coding. #R##N#In this work we show that usage of large data blocks may strictly improve upon the trivial encoding which treats each bit in the block independently. To this end, we provide general bounds on \beta_t, and prove that for any constant C there is an explicit broadcast setting in which \beta = 2 but \beta_1 > C. One of these examples answers a question of Lubetzky and Stav. #R##N#In addition, we provide examples with the following counterintuitive direct-sum phenomena. Consider a union of several mutually independent broadcast settings. The optimal code for the combined setting may yield a significant saving in communication over concatenating optimal encodings for the individual settings. This result also provides new non-linear coding schemes which improve upon the largest known gap between linear and non-linear Network Coding, thus improving the results of Dougherty, Freiling, and Zeger. #R##N#The proofs use ideas related to Witsenhausen's rate, OR graph products, colorings of Cayley graphs and the chromatic numbers of Kneser graphs.

Connected through hyperedge HE-1 (induced by source paper "privacy utility tradeoff in a guessing framework inspired by index coding"):
  with members:
    - "an operational approach to information leakage"
    - "from the information bottleneck to the privacy funnel"
    - "lexicographic products and the power of non linear network coding"

Connected through hyperedge HE-2 (induced by source paper "decentralized coded caching without file splitting"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "wireless caching technical misconceptions and business barriers"
    - "big data caching for networking moving from cloud to edge"
    - "an efficient coded multicasting scheme preserving the multiplicative caching gain"
    - "finite length analysis of caching aided coded multicasting"
    - "effect of number of users in multi level coded caching"

Connected through hyperedge HE-3 (induced by source paper "characteristic dependent linear rank inequalities via complementary vector spaces"):
  with members:
    - "characteristic dependent linear rank inequalities with applications to network coding"
    - "achievable rate regions for network coding"
    - "lexicographic products and the power of non linear network coding"
    - "graph theoretical constructions for graph entropy and network coding based communications"
    - "linear rank inequalities on five or more variables"

Connected through hyperedge HE-4 (induced by source paper "on locally decodable index codes"):
  with members:
    - "reduced complexity index codes and improved upperbound on broadcast rate for neighboring interference problems"
    - "privacy in index coding k limited access schemes"
    - "speeding up distributed machine learning using codes"
    - "preserving privacy while broadcasting k limited access schemes"
    - "local partial clique covers for index coding"
    - "on a duality between recoverable distributed storage and index coding"
    - "graph theory versus minimum rank for index coding"
    - "topological interference management through index coding"

Connected through hyperedge HE-5 (induced by source paper "a bound on the shannon capacity via a linear programming variation"):
  with members:
    - "on approximating the sum rate for multiple unicasts"
    - "local graph coloring and index coding"

Connected through hyperedge HE-6 (induced by source paper "the optimality of partial clique covering for index coding"):
  with members:
    - "interlinked cycles for index coding generalizing cycles and cliques"
    - "local partial clique covers for index coding"
    - "approximate capacity of index coding for some classes of graphs"
    - "the single uniprior index coding problem the single sender case and the multi sender extension"
    - "tdma is optimal for all unicast dof region of tim if and only if topology is chordal bipartite"
    - "fractional coloring orthogonal access achieves all unicast capacity dof region of index coding tim if and only if network topology is chordal"
    - "structural properties of index coding capacity using fractional graph theory"
    - "storage capacity of repairable networks"

Connected through hyperedge HE-7 (induced by source paper "structural characteristics of two sender index coding"):
  with members:
    - "cooperative multi sender index coding"
    - "optimal linear broadcast rates of the two sender unicast index coding problem with fully participated interactions"
    - "on the optimal broadcast rate of the two sender unicast index coding problem with fully participated interactions"
    - "optimal scalar linear codes for some classes of the two sender groupcast index coding problem"
    - "fundamental limits of decentralized data shuffling"
    - "interlinked cycles for index coding generalizing cycles and cliques"
    - "optimal finite length and asymptotic index codes for five or fewer receivers"
    - "on the capacity for distributed index coding"

Connected through hyperedge HE-8 (induced by source paper "private information retrieval with side information"):
  with members:
    - "private broadcasting an index coding approach"
    - "pir array codes with optimal virtual server rate"
    - "the capacity of robust private information retrieval with colluding databases"
    - "private information retrieval from coded databases with colluding servers"
    - "the capacity of private information retrieval"
    - "the capacity of private information retrieval from coded databases"
    - "pir with low storage overhead coding instead of replication"
    - "private information retrieval for coded storage"

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)