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: "beyond the cut set bound uncertainty computations in network coding with correlated sources"
  Abstract: Cut-set bounds are not, in general, tight for all classes of network communication problems. In this paper, we introduce a new technique for proving converses for the problem of transmission of correlated sources in networks, which results in bounds that are tighter than the corresponding cut-set bounds. We also define the concept of “uncertainty region” which might be of independent interest. We provide a full characterization of this region for the case of two correlated random variables. The bounding technique works as follows: on one hand, we show that if the communication problem is solvable, the uncertainty of certain random variables in the network with respect to imaginary parties that have partial knowledge of the sources must satisfy some constraints that depend on the network architecture. On the other hand, the same uncertainties have to satisfy constraints that only depend on the joint distribution of the sources. Matching these two leads to restrictions on the statistical joint distribution of the sources in communication problems that are solvable over a given network architecture. Our technique also provides nontrivial outer bounds for communication problems with secrecy constraints.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of computation broadcast"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "the capacity of private computation"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "index coding with coded side information"
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "interference channels with arbitrarily correlated sources"

Connected through hyperedge HE-2 (induced by source paper "capacity bounds for networks with correlated sources and characterisation of distributions by entropies"):
  with members:
    - "cut set bounds on network information flow"
    - "characterising probability distributions via entropies"
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "network coding capacity a functional dependence bound"
    - "dualities between entropy functions and network codes"
    - "network information flow with correlated sources"

Connected through hyperedge HE-3 (induced by source paper "cut set bounds on network information flow"):
  with members:
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "network coding capacity a functional dependence bound"
    - "the minimal set of ingleton inequalities"
    - "dualities between entropy functions and network codes"
    - "network information flow with correlated sources"

Connected through hyperedge HE-4 (induced by source paper "characterising probability distributions via entropies"):
  with members:
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"

Connected through hyperedge HE-5 (induced by source paper "on the capacity of multiplicative finite field matrix channels"):
  with members:
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "the capacity of random linear coding networks as subspace channels"
    - "coding for linear operator channels over finite fields"
    - "communication over finite field matrix channels"
    - "coding for errors and erasures in random network coding"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Connected through hyperedge HE-6 (induced by source paper "on the capacity of networks with correlated sources"):
  with members:
    - "beyond the cut set bound uncertainty computations in network coding with correlated sources"
    - "dualities between entropy functions and network codes"

Connected through hyperedge HE-7 (induced by source paper "beyond the cut set bound uncertainty computations in network coding with correlated sources"):
  with members:
    - "on secure network coding with uniform wiretap sets"
    - "multishot codes for network coding using rank metric codes"
    - "network coding capacity a functional dependence bound"
    - "packing and covering properties of subspace codes for error control in random linear network coding"
    - "a rank metric approach to error control in random network coding"
    - "coding for errors and erasures in random network coding"
    - "packing and covering properties of rank metric codes"
    - "on coding for reliable communication over packet networks"

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)