human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "function computation through a bidirectional relay"):
  Abstract: We consider a function computation problem in a three node wireless network. Nodes A and B observe two correlated sources $X$ and $Y$ respectively, and want to compute a function $f(X,Y)$. To achieve this, nodes A and B send messages to a relay node C at rates $R_A$ and $R_B$ respectively. The relay C then broadcasts a message to A and B at rate $R_C$. We allow block coding, and study the achievable region of rate triples under both zero-error and $\epsilon$-error. As a preparation, we first consider a broadcast network from the relay to A and B. A and B have side information $X$ and $Y$ respectively. The relay node C observes both $X$ and $Y$ and broadcasts an encoded message to A and B. We want to obtain the optimal broadcast rate such that A and B can recover the function $f(X,Y)$ from the received message and their individual side information $X$ and $Y$ respectively. For a special case of $f(X,Y) = X \oplus Y$, we show equivalence between $\epsilon$-error and zero-error computations-- this gives a rate characterization for zero-error XOR computation in the broadcast network. As a corollary, this also gives a rate characterization for zero-error in the relay network when the support set of $p_{XY}$ is full. For the relay network, the zero-error rate region for arbitrary functions is characterized in terms of graph coloring of some suitably defined probabilistic graphs. We then give a single-letter inner bound to this rate region. Further, we extend the graph theoretic ideas to address the $\epsilon$-error problem and obtain a single-letter inner bound.

Includes members:
  Member "structural properties of index coding capacity using fractional graph theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative multi sender index coding")
  Member "an equivalence between network coding and index coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "embedded index coding")
  Member "index coding an interference alignment perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "on network coding for sum networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics")
  Member "network coding is zero error always possible":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lossless source coding in the point to point multiple access and random access scenarios")
  Member "optimal function computation in directed and undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to distribute computation in networks")
  Member "two way source coding with a helper":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive coding for two way lossy source channel communication")
  Member "universal coding for correlated sources with complementary delivery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "broadcast function computation with complementary side information")

Question: Please tell me which class the source paper 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)