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 "parity queries for binary classification"):
  Abstract: Consider a query-based data acquisition problem that aims to recover the values of $k$ binary variables from parity (XOR) measurements of chosen subsets of the variables. Assume the response model where only a randomly selected subset of the measurements is received. We propose a method for designing a sequence of queries so that the variables can be identified with high probability using as few ($n$) measurements as possible. We define the query difficulty $\bar{d}$ as the average size of the query subsets and the sample complexity $n$ as the minimum number of measurements required to attain a given recovery accuracy. We obtain fundamental trade-offs between recovery accuracy, query difficulty, and sample complexity. In particular, the necessary and sufficient sample complexity required for recovering all $k$ variables with high probability is $n = c_0 \max\{k, (k \log k)/\bar{d}\}$ and the sample complexity for recovering a fixed proportion $(1-\delta)k$ of the variables for $\delta=o(1)$ is $n = c_1\max\{k, (k \log(1/\delta))/\bar{d}\}$, where $c_0, c_1>0$.

Includes members:
  Member "unequal error protection querying policies for the noisy 20 questions problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resolution limits of noisy 20 questions estimation")
  Member "community recovery in hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crowdsourced classification with xor queries fundamental limits and an efficient algorithm")
  Member "predicting positive and negative links with noisy queries theory practice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "fundamental limits on data acquisition trade offs between sample complexity and query difficulty":
    also connected through other hyperedges:
      (none)
  Member "the set of solutions of random xorsat formulae":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interactive particle systems on hypergraphs drift analysis and the walksat algorithm")
  Member "information recovery from pairwise measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi frequency phase synchronization")
  Member "decoding binary node labels from censored edge measurements phase transition and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")

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)