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: "batched sparse codes"
  Abstract: Network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing. However, using network coding usually incurs high computational and storage costs in the network devices and terminals. For example, some network coding schemes require the computational and/or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission, making such schemes difficult to be implemented in a router-like device that has only constant computational and storage capacities. In this paper, we introduce batched sparse code (BATS code), which enables a digital fountain approach to resolve the above issue. BATS code is a coding scheme that consists of an outer code and inner code. The outer code is a matrix generation of a fountain code. It works with the inner code that comprises random linear coding at the intermediate network nodes. BATS codes preserve such desirable properties of fountain codes as ratelessness and low encoding/decoding complexity. The computational and storage capacities of the intermediate network nodes required for applying BATS codes are independent of the number of packets for transmission. Almost capacity-achieving BATS code schemes are devised for unicast networks and certain multicast networks. For general multicast networks, under different optimization criteria, guaranteed decoding rates for the destination nodes can be obtained.

Connected through hyperedge HE-1 (induced by source paper "upper bound scalability on achievable rates of batched codes for line networks"):
  with members:
    - "throughput and latency in finite buffer line networks"
    - "sparse network coding with overlapping classes"

Connected through hyperedge HE-2 (induced by source paper "decentralized coded caching without file splitting"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "wireless caching technical misconceptions and business barriers"
    - "big data caching for networking moving from cloud to edge"
    - "an efficient coded multicasting scheme preserving the multiplicative caching gain"
    - "finite length analysis of caching aided coded multicasting"
    - "effect of number of users in multi level coded caching"

Connected through hyperedge HE-3 (induced by source paper "an overflow problem in network coding for secure cloud storage"):
  with members:
    - "toward optimal secure distributed storage systems with exact repair"
    - "on secrecy capacity of minimum storage regenerating codes"
    - "security in locally repairable storage"
    - "novel repair by transfer codes and systematic exact mbr codes with lower complexities and smaller field sizes"
    - "data secrecy in distributed storage systems under exact repair"
    - "capacity and security of heterogeneous distributed storage systems"
    - "simple regenerating codes network coding for cloud storage"
    - "information theoretically secure regenerating codes for distributed storage"

Connected through hyperedge HE-4 (induced by source paper "v2x content distribution based on batched network coding with distributed scheduling"):
  with members:
    - "collaborative downloading in vanet using network coding"

Connected through hyperedge HE-5 (induced by source paper "a markov chain model for the decoding probability of sparse network coding"):
  with members:
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "fulcrum network codes a code for fluid allocation of complexity"
    - "effects of mac approaches on non monotonic saturation with cope a simple case study"
    - "network coding meets tcp"

Connected through hyperedge HE-6 (induced by source paper "coding for network coded slotted aloha"):
  with members:
    - "linearly coupled fountain codes"
    - "an algebraic approach to physical layer network coding"
    - "network coded multiple access"
    - "frameless aloha protocol for wireless networks"
    - "high throughput random access via codes on graphs coded slotted aloha"
    - "physical layer network coding tutorial survey and beyond"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-7 (induced by source paper "two phase cooperative broadcasting based on batched network code"):
  with members:
    - "finite length analysis of bats codes"

Connected through hyperedge HE-8 (induced by source paper "expander chunked codes"):
  with members:
    - "overhead optimized gamma network codes"
    - "linear complexity overhead optimized random linear network codes"
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"
    - "throughput and latency in finite buffer line networks"
    - "overlapped chunked network coding"
    - "sparse network coding with overlapping classes"
    - "packing and covering properties of subspace codes for error control in random linear network coding"
    - "coding schemes for line 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)