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: "designing dedicated data compression for physics experiments within fpga already used for data acquisition"
  Abstract: Physics experiments produce enormous amount of raw data, counted in petabytes per day. Hence, there is large effort to reduce this amount, mainly by using some filters. The situation can be improved by additionally applying some data compression techniques: removing redundancy and optimally encoding the actual information. Preferably, both filtering and data compression should fit in FPGA already used for data acquisition - reducing requirements of both data storage and networking architecture. #R##N#We will briefly explain and discuss some basic techniques, for a better focus applied to design a dedicated data compression system basing on a sample data from a prototype of a tracking detector: 10000 events for 48 channels. We will focus on the time data here, which after neglecting the headers and applying data filtering, requires on average 1170 bits/event using the current coding. Encoding relative times (differences) and grouping data by channels, reduces this number to 798 bits/channel, still using fixed length coding: a fixed number of bits used for a given value. Using variable length Huffman coding to encode numbers of digital pulses for a channel and the most significant bits of values (simple binning) reduces further this number to 552 bits/event. Using adaptive binning: denser for frequent values, and an accurate entropy coder we get further down to 455 bits/event - this option can easily fit unused resources of FPGA currently used for data acquisition. Finally, using separate probability distributions for different channels, what could be done by a software compressor, leads to 437bits/event, what is 2.67 times less than the original 1170 bits/event.

Connected through hyperedge HE-1 (induced by source paper "gaussian autoencoder"):
  with members:
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "cramer wold autoencoder"

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)