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: "network coding capacity regions via entropy functions"
  Abstract: In this paper, we use entropy functions to characterise the set of rate-capacity tuples achievable with either zero decoding error, or vanishing decoding error, for general network coding problems. We show that when sources are colocated, the outer bound obtained by Yeung, A First Course in Information Theory, Section 15.5 (2002) is tight and the sets of zero-error achievable and vanishing-error achievable rate-capacity tuples are the same. We also characterise the set of zero-error and vanishing-error achievable rate capacity tuples for network coding problems subject to linear encoding constraints, routing constraints (where some or all nodes can only perform routing) and secrecy constraints. Finally, we show that even for apparently simple networks, design of optimal codes may be difficult. In particular, we prove that for the incremental multicast problem and for the single-source secure network coding problem, characterisation of the achievable set is very hard and linear network codes may not be optimal.

Connected through hyperedge HE-1 (induced by source paper "a local perspective on the edge removal problem"):
  with members:
    - "a class of non linearly solvable networks"
    - "on the impact of a single edge on the network coding capacity"
    - "network coding is zero error always possible"
    - "lexicographic products and the power of non linear network coding"
    - "dualities between entropy functions and network codes"

Connected through hyperedge HE-2 (induced by source paper "secure network coding in the setting in which a non source node may generate random keys"):
  with members:
    - "on secure network coding with nonuniform or restricted wiretap sets"
    - "on capacity region of wiretap networks"
    - "universal secure network coding via rank metric codes"

Connected through hyperedge HE-3 (induced by source paper "secure multi source multicast"):
  with members:
    - "universal secure multiplex network coding with dependent and non uniform messages"
    - "sufficiently myopic adversaries are blind"
    - "probability of partially solving random linear systems in network coding"
    - "probability of partially decoding network coded messages"
    - "can network coding bridge the digital divide in the pacific"
    - "weakly secure regenerating codes for distributed storage"
    - "universal secure network coding via rank metric codes"
    - "optimal exact regenerating codes for distributed storage at the msr and mbr points via a product matrix construction"

Connected through hyperedge HE-4 (induced by source paper "capacity and achievable rate regions for linear network coding over ring alphabets"):
  with members:
    - "linear network coding over rings part ii vector codes and non commutative alphabets"
    - "linear network coding over rings part i scalar codes and commutative alphabets"
    - "a class of non linearly solvable networks"
    - "on vector linear solvability of multicast networks"
    - "on achievability of an r l fractional linear network code"
    - "the two unicast problem"
    - "characteristic dependent linear rank inequalities with applications to network coding"
    - "achievable rate regions for network coding"

Connected through hyperedge HE-5 (induced by source paper "strong converses are just edge removal properties"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "on the capacity advantage of a single bit"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "strong converse theorems for degraded broadcast channels with feedback"
    - "equivalence for networks with adversarial state"

Connected through hyperedge HE-6 (induced by source paper "on the capacity advantage of a single bit"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "can negligible cooperation increase network reliability"
    - "network coding is zero error always possible"

Connected through hyperedge HE-7 (induced by source paper "a numerical study on the wiretap network with a simple network topology"):
  with members:
    - "connecting multiple unicast and network error correction reduction and unachievability"
    - "characterizing the rate region of the 4 3 3 exact repair regenerating codes"
    - "on secure network coding with nonuniform or restricted wiretap sets"
    - "on secure network coding with uniform wiretap sets"
    - "performance bounds on a wiretap network with arbitrary wiretap sets"
    - "linear rank inequalities on five or more variables"

Connected through hyperedge HE-8 (induced by source paper "capacity results for multicasting nested message sets over combination networks"):
  with members:
    - "is non unique decoding necessary"
    - "wireless network information flow a deterministic approach"
    - "the single source two terminal network with network coding"
    - "entropy vectors and network codes"

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)